Study of the Earliest Stages of Galactic Star Formation:

BLAST Survey of the Vela Molecular Ridge

Daniel Angles Alcazar

A thesis submitted in partial fulfillmentof the requirements for the degree of

Master of Science in Physics

University of Puerto Rico, Rio Piedras Campus

2009

Program Authorized to OÆer Degree: Department of Physics

University of Puerto Rico, Rio Piedras CampusGraduate School

This is to certify that I have examined this copy of a master’s thesis by

Daniel Angles Alcazar

and have found that it is complete and satisfactory in all respects,and that any and all revisions required by the final

examining committee have been made.

Chair of the Supervisory Committee:

Professor Luca Olmi

Reading Committee:

Professor Daniel Altschuler

Professor Carmen Pantoja

Date:

In presenting this thesis in partial fulfillment of the requirements for a master’s degreeat the University of Puerto Rico, Rio Piedras Campus, I agree that the Library shallmake its copies freely available for inspection. I further agree that extensive copyingof this thesis is allowable only for scholarly purposes, consistent with “fair use” asprescribed in the U.S. Copyright Law. Any other reproduction for any purpose or byany means shall not be allowed without my written permission.

Signature

Date

University of Puerto Rico, Rio Piedras Campus

Abstract

Study of the Earliest Stages of Galactic Star Formation:BLAST Survey of the Vela Molecular Ridge

Daniel Angles Alcazar

Chair of the Supervisory Committee:Professor Luca Olmi

Department of Physics

Stars form from the gravitational collapse of dense clouds of gas and dust in the

interstellar molecular medium. However, little is known about the origin and evo-

lution of these early stages of star formation. In this work, we present a detailed

multi-wavelength characterization of a sample of these dense cores detected by the

Balloon-borne Large-Aperture Submillimeter Telescope (BLAST) in the Vela-D molec-

ular cloud. Combining the BLAST photometry at 250, 350, and 500 µm with addi-

tional existing observations from millimeter to mid-infrared wavelengths, we have

constrained the spectral energy distribution of 141 cores. Their physical parameters

have been estimated assuming an isothermal modified blackbody model. In addition,

associations of BLAST cores with mid-infrared sources allow us to separate starless

from proto-stellar cores. We find that proto-stellar cores are characterized by higher

luminosities and temperatures than starless cores, indicating an evolutionary sequence.

TABLE OF CONTENTS

Page

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Chapter 1: Star Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 The Interstellar Medium . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 From Molecular Clouds to Pre-Stellar Cores . . . . . . . . . . . . . . . 6

1.4 The Observation of Dense Cores . . . . . . . . . . . . . . . . . . . . . . 10

Chapter 2: Submillimeter Observations and the BLAST Telescope . . . . . . 14

2.1 Methods in Submillimeter Astronomy . . . . . . . . . . . . . . . . . . . 14

2.2 The BLAST Telescope . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Chapter 3: Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.1 The Vela Molecular Ridge . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.2 BLAST Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.3 Archive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Chapter 4: Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.1 Overview of the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.2 1200–8µm Source Identification . . . . . . . . . . . . . . . . . . . . . . 59

4.3 Source Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4 Spectral Energy Distribution of the Dense Cores . . . . . . . . . . . . . 67

4.5 Bonnor-Ebert Masses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

i

Chapter 5: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.1 SEDs and Morphology of the Cores . . . . . . . . . . . . . . . . . . . . 76

5.2 Separating Starless and Proto-Stellar Cores . . . . . . . . . . . . . . . . 78

5.3 Distribution of Physical Parameters . . . . . . . . . . . . . . . . . . . . 83

5.4 Do Starless and Proto-Stellar Cores Have DiÆerent Properties? . . . . . 91

5.5 Mass Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.6 Dynamical State of the Cores . . . . . . . . . . . . . . . . . . . . . . . 97

5.7 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 100

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

ii

LIST OF FIGURES

Figure Number Page

1.1 Evolutionary sequence of low-mass star formation . . . . . . . . . . . . 3

2.1 Atmospheric transmission . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 DiÆraction from a circular aperture . . . . . . . . . . . . . . . . . . . . 19

2.3 Schematic of a bolometer . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4 BLAST broadband filters . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.5 Model of the BLAST06 gondola . . . . . . . . . . . . . . . . . . . . . . 26

2.6 Schematic of the BLAST gondola . . . . . . . . . . . . . . . . . . . . . 28

2.7 Spectral energy distribution of the BLAST06 calibrator . . . . . . . . . 31

2.8 BLAST06 point spread functions . . . . . . . . . . . . . . . . . . . . . 31

3.1 12CO map of the VMR . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 The VMR and the Vela SNR . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3 BLAST image of the VMR . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.4 BLAST 250µm image of Vela-D . . . . . . . . . . . . . . . . . . . . . . 43

3.5 Size distribution of the BLAST cores . . . . . . . . . . . . . . . . . . . 45

3.6 SIMBA map of Vela-D . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.7 Color-composite image of Vela-D at 250, 70, and 24 µm . . . . . . . . . 51

3.8 IRAC map of Vela-D . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.1 SED and thumbnails of a warm core . . . . . . . . . . . . . . . . . . . 77

5.2 SED and thumbnails of a cold core . . . . . . . . . . . . . . . . . . . . 77

5.3 Thumbnails of starless/pre-stellar cores . . . . . . . . . . . . . . . . . . 82

5.4 Distributions of physical parameters . . . . . . . . . . . . . . . . . . . . 85

5.5 Color–color plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.6 Luminosity–mass plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.7 Spatial distribution of starless and proto-stellar cores . . . . . . . . . . 90

iii

5.8 Mass spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.9 Bonnor-Ebert masses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

iv

GLOSSARY

BE: Bonnor-Ebert

BLAST: Ballooon-borne Large Aperture Submillimeter Telescope.

CMF: Core Mass Function.

DC: Direct Current.

EM: Electromagnetic.

FIR: Far-Infrared.

FWHM: Full Width at Half Maximum.

GMC: Giant Molecular Cloud.

GPS: Global Positioning System.

IGA: IRAS Galaxy Atlas.

IMF: Initial Mass Function.

v

IR: Infrared.

IRAC: Infrared Array Camera.

IRAM: Institut de Radio Astronomie Millimetrique.

IRAS: Infrared Astronomical Satellite.

ISM: Interstellar Medium.

ISO: Infrared Space Observatory.

ISSA: IRAS Sky Survey Atlas.

LDB: Long Duration Balloon.

MAMBO: Max-Planck Millimeter Bolometer.

2MASS: Two Micron All Sky Survey.

MIPS: Multiband Imaging Photometer for Spitzer.

MIR: Mid-infrared.

MSX: Midcourse Space Experiment.

NIR: Near-Infrared.

vi

PAH: Polycylic Aromatic Hydrocarbon.

PSC: Point Source Catalogue.

PSF: Point Spread Function.

SANEPIC: Signal and Noise Estimation Procedure Correlations.

SCUBA: Submillimetre Common-User Bolometer Array.

SED: Spectral Energy Distribution.

SEST: Swedish ESO Submillimeter Telescope.

SHARC-II: Submillimeter High Angular Resolution Camera.

SIMBA: SEST Imaging Bolometer Array.

SNR: Supernova Remnant.

SWIRE: Spitzer Wide-area Infrared Extragalactic Survey.

UV: Ultraviolet Radiation.

VMR: Vela Molecular Ridge.

YSO: Young Stellar Object.

vii

ACKNOWLEDGMENTS

First of all, I would like to acknowledge my thesis advisor, Luca Olmi, for his

continuous guidance, support, and patient during these two years. I also would like to

thank my advisor and my reading committee members, Daniel Altschuler and Carmen

Pantoja, for their helpful suggestions and corrections for the completion of this thesis.

I am very grateful to Gerardo Morell and the NASA Puerto Rico Space Grant Con-

sortium for their continuous support. I also would like to acknowledge the graduate

students, faculty, and staÆ of the Department of Physics for their thoughtful assis-

tance. In particular, thanks to Jorge Morales for his companionship during hundreds

of hours in front of a computer.

I want to thank my friends from Torre Norte: Baldomero, Victorius, Josete, Ruben,

Pascal, Fumero, Noel, and Julissa for an unforgettable first year in Puerto Rico (thanks

to them I could spend the rest of the time researching). I also would like to thank my

family for all their personal support, and Jose, Fanny, “los gordillos”, and Colibri, for

being my family here.

Finally, the most special thanks go to Julissa, my wife, for her unconditional

support and because she makes me happy every day.

viii

A mi princesita...

ix

INTRODUCTION

Star formation is one of the most important research areas in modern Astrophysics.

Understanding the formation of stars, particularly in its earliest phases, is still a fun-

damental and partially unsolved problem, which also has profound consequences on

both planet formation and the physics of galaxies. A considerable theoretical and

observational eÆort is currently being done in order to understand the initial condi-

tions and mechanisms leading to the formation of stars from the gas and dust in the

interstellar medium. Star form out of huge volumes of gas known as molecular clouds,

composed predominantly by molecular hydrogen, H2

. Along with their gas, molec-

ular clouds also contain an admixture of small solid particles, known as interstellar

dust grains. These particles e±ciently absorb visible and infrared light and reradiate

this energy at longer wavelengths, particularly in the submillimeter regime (ª 200–

1000µm). Molecules are imperfect tracers because they undergo complex physical

and chemical changes. In contrast, dust is stable in most phases of the interstellar

medium and its emission at submillimeter wavelengths gives a direct measure of the

mass content along the line of sight. Ground-based observations in the submillime-

ter regime are partially or completely limited by the atmospheric absorption and its

x

rapid changes in time and along diÆerent line of sights. Cold dust emission is observed

much more e±ciently from telescopes on (sub)orbital platforms, above all or most of

the atmosphere. Recently, the Balloon-borne Large-Aperture Submillimeter Telescope

(BLAST) has obtained the most sensitive large-scale maps of the sky at 250, 350, and

500µm to date.

The aim of this thesis is to exploit the submillimeter survey performed by BLAST

towards the Vela constellation in order to investigate the physical properties of the

earliest stages of star formation. In this work we make use of additional existing

observations from mid-infrared to millimeter wavelengths (including SEST-SIMBA,

IRAS, AKARI, Spitzer MIPS, IRAC, and MSX data) to properly characterize the

dense cores detected by BLAST. This multi-wavelength approach is needed given the

wide range of scales and physical conditions in which star formation occurs.

In Chapter 1 we introduce the current accepted model for the formation of low-

and intermediate-mass stars. Then, we discuss in more depth some fundamental as-

pects regarding the study of the earliest stages of star formation, from giant molecular

clouds to pre-stellar cores. Some observational aspects of submillimeter Astronomy

are presented in Chapter 2, where we also present a general description of the BLAST

telescope. In Chapter 3 we introduce the region analyzed in this work, the Vela-D

cloud. Then, the specific observations performed towards this cloud are described in

details, with special emphasis on the maps and source catalogs used in this work. An

xi

overview of the data analysis is given in Chapter 4. Here, we describe the specific

methods used to correlate the information at diÆerent wavelengths, constraining the

spectral energy distribution of the dense cores, and estimating their physical param-

eters. Finally, we discuss the results of this work and summarize our conclusions in

Chapter 5.

This thesis is based on material not yet published. Part of the work presented here

will appear in Netterfield et al. (2009) and Olmi et al. (2009).

xii

1

Chapter 1

STAR FORMATION

Over the past two decades, a reasonably robust evolutionary sequence has been es-

tablished and widely accepted for the formation and evolution of low- and intermediate-

mass stars (M ∑ 8 MØ) within molecular clouds (e.g., Shu et al. 1987, 2004; McKee

and Ostriker 2007, and references therein). However, several fundamental questions

related to the earliest stages of star formation and the formation of high-mass stars

remain unanswered. The formation of high-mass stars is not a simple scaled-up ver-

sion of low-mass star formation, it is subject to further theoretical and observational

di±culties (Zinnecker and Yorke 2007), and will not be specifically discussed here.

1.1 Introduction

Stars form from the gravitational collapse of dense cores in molecular clouds. In the

current accepted scenario of low-mass star formation, the fragmentation of the parent

cloud into cold, dense condensations leads to the formation of gravitationally-bound

cores, which are initially supported against gravity by a combination of thermal, tur-

bulent, and magnetic pressures. Subsequently, these pre-stellar cores/condensations

become gravitationally unstable and evolve toward the formation of an accreting pro-

2

tostar : a central compact object in hydrostatic equilibrium surrounded by an accretion

disk and embedded within an infalling envelope of dust and gas. While the central

compact object continues accreting material, the gradual dissipation of the disk and

envelop characterize the evolutionary phase of the system.

Fig. 1.1 shows schematically the theoretical evolutionary sequence of low-mass

star formation, from pre-stellar to proto-stellar and finally pre-main sequence phases.

DiÆerent stages are observationally characterized by diÆerent shapes of the spectral

energy distribution (SED, see section 2.1.4), and the bolometric temperature (the

temperature of a blackbody whose spectrum has the same mean frequency as the ob-

served SED). Dense cores are observable at millimeter and submillimeter wavelengths,

but are opaque in the near-infrared (NIR) and mid-infrared (MIR) bands (e.g. Alves

et al. 2001). Proto-stellar and pre-main sequence objects are classified according to

the relative mass distribution among the envelope, the circumstellar disk, and the

central compact object. Observationally, we distinguish among Class 0 to Class III

protostars, from less to more evolved stages (see Lada and Wilking 1984; Andre et al.

1993, 2000). Previous IR studies using IRAS, MSX, and ISO satellites in addition to

ground-based NIR surveys have provided a complete census of Class I–III sources (e.g.,

Bontemps et al. 2001). However, only a few bona-fide Class 0 protostars are known

to date. Observations at longer wavelengths are essential to detect and characterize

even earlier evolutionary stages.

3

Figure 1.1: Theoretical evolutionary sequence of low-mass star formation and theobservational classification scheme (De Luca 2008).

4

1.2 The Interstellar Medium

The interstellar medium (ISM) is a mixture of ª 99% gas and ª 1% dust by mass

which permeates the space between stars. Interstellar hydrogen amounts ª 89% of the

gas content in the ISM1 and it is found in a variety of chemical forms, temperatures,

and densities which characterize diÆerent phases coexisting in the ISM (see, e.g.,

Stahler and Palla 2004).

The largest portion of Galactic mass (ª 34% in volume) is in the form of (neutral)

atomic hydrogen gas, observable in the 21 cm radio spectral line. This neutral gas

is believed to be composed of two diÆerent phases in pressure equilibrium, diÆuse

cold clouds (called HI regions; T ª 80K) confined by a surrounding, hotter (ª 8 £

103 K), and rarefied intercloud medium. In addition, it is thought that ª 65% of the

Galaxy volume is filled with a hot and extremely hot (ª 8 £ 103 K and ª 5 £ 105 K

respectively) low density ionized gas primarily originated from supernova explosions.

Thus, the physical conditions of neutral and ionized phases of the ISM are far from

ideal regarding the formation of stars which therefore must be restricted to particular

regions of the ISM.

The appropriate conditions for the gravitational collapse of interstellar gas into

stars are found in the molecular phase of the ISM, characterized by higher densities

(> 300 particles/cm3) and very low temperatures (ª 10K) compared to the neutral

and ionized phases (molecules facilitate the gravitational collapse emitting excess grav-

1Percentage by number of nuclei; ª 9% is helium and ª 2% heavier elements.

5

itational energy as electromagnetic radiation in a process called “cooling radiation”).

Molecular clouds comprise a significant fraction of the mass content in the Galaxy

in ª 1% of its volume and, unlike other phases of the ISM, they are not necessarily

in pressure equilibrium with the environment since self-gravity governs the cloud’s

mechanical equilibrium.

Molecular hydrogen (H2

), being the principal component of molecular clouds, is

an homonuclear diatomic molecule and therefore its rotational transitions are forbid-

den. Thus, molecular clouds are usually observed in radio rotational lines of tracers

such as CO, OH, and other molecules, providing information about the gas spatial

distribution from line integrated intensity maps, and its dynamical state from ve-

locity gradients determined from doppler shifts. Furthermore, molecular clouds are

also indirectly visible in optical and NIR images as obscured regions in rich stellar

fields. Dust grains, though much less abundant than gas, are responsible for most

of the interstellar extinction and reddening of the optical/NIR light from background

stars. Remarkably, this eÆect is commonly used to obtain extended maps of molecular

clouds. In addition, dust grains, which in the regions of interest to us can be consid-

ered in thermal equilibrium, re-radiate the absorbed optical/NIR emission at longer

wavelengths. Therefore, with a characteristic Black-Body continuous spectrum, the

peak of the dust emission occurs at ∏max [cm] = 0.29£T°1 [K], which corresponds to

the submillimeter band at the typical temperatures in molecular clouds.

6

Cloud Type M n T

[MØ] [cm°3] [K]

GMC ª 104 ° 105 ª 102 ª 15

Clump ª 102 ° 103 ª 103 ° 104 ª 10° 20

Core ª 1° 10 ª 104 ° 105 ª 10° 30

Table 1.1: Physical properties of molecular clouds (adapted from Stahler and Palla2004).

1.3 From Molecular Clouds to Pre-Stellar Cores

Molecular clouds, often characterized by a complex morphology, are found in a wide

range of sizes and masses. For practical reasons, they can be separated into diÆerent

types which are summarized in Table 1.1.

Giant molecular clouds (GMC) are the largest coherent structures in the Galaxy,

with sizes ª 50 pc, masses ª 105 MØ, and containing ª 80% of the molecular hy-

drogen. These large reservoirs of gas and dust, preferentially located within Galactic

arms, are closely related to the formation of massive stars. Actually, every observed

Galactic OB association (group of stars with members of M > 8 MØ) seems to be

physically associated with a GMC. Direct evidence is found in the habitual presence

of HII regions in GMCs2. Furthermore, massive stars play a crucial role in the evolu-

2Regions of ionized hydrogen (HII) within GMCs are produced by the ultraviolet (UV) radiationemitted from young massive stars. They are observed in radio continuum emission (from free-freeelectron transitions) and hydrogen line emission (from electron-ion recombination, including theoptical Balmer series of hydrogen).

7

tion of GMCs, and it is thought that the intense winds powered by embedded O and

B stars constrain their lifetimes to ª 3£ 107 years (Stahler and Palla 2004).

The origin and evolution of GMCs is still not well understood. Various molecu-

lar tracers probe regions with diÆerent densities, revealing a complex internal struc-

ture and dynamics. They appear to be composed of large fragments with masses

ª 102°103 MØ and sizes ª 0.5 pc, called clumps, connected by filaments, and smaller,

denser sub-units called cores, with typical masses ª 1°10 MØ and sizes ª 0.1 pc. The

distinction between cores and clumps is often related to diÆerent modes of star forma-

tion: individual cores lead to the formation of one or few stars (isolated star formation)

while clumps are related to the formation of clusters of stars (cluster star formation).

However, we note that there is no net distinction between them; in fact, observations

with enough resolution may separate clumps into individual cores. Ultimately, the

detection of infrared (IR) point sources driving jets and outflows inside dense cores

suggests that these are the actual sites of ongoing star formation. The formation and

evolution of dense cores in molecular clouds is, thus, critical for understanding the

earliest stages of star formation.

The virial theorem may be used to estimate the initial conditions needed for the

gravitational collapse of clouds. The minimum mass necessary for a spherical homo-

geneous cloud with temperature, T , and density, Ω, to initiate a spontaneous collapse

is known as the Jeans mass :

Mcloud > MJeans =

µ5kT

GµmH

∂3/2

µ3

4ºΩ0

∂1/2

, (1.1)

8

where, G and k are the gravitational and Boltzmann constants respectively, µ is

the mean molecular weight, and mH the mass of the hydrogen atom. Assuming an

isothermal contraction (i.e., the gravitational energy released during the collapse is

radiated away e±ciently), the cloud would collapse in a characteristic free-fall time

independent of the initial size of the cloud:

tff =

µ3º

32

1

GΩ

∂1/2

. (1.2)

Given the mean density and temperature in a typical GMC, equation 1.1 implies

that the entire cloud should be collapsing. The internal structure observed in GMCs,

however, does not support the idea of large scale collapsing of clouds, suggesting that

initial inhomogeneities in density could originate a gravitational fragmentation into

clumps and cores, which would individually satisfy the Jeans mass limit. In addition,

molecular line observations show that the clump velocities within GMCs appear to

be random and not directed toward a collapsing center. Molecular clouds, thus, seem

to be in approximate force balance over their lifetimes, supported against gravity

by additional mechanisms besides thermal pressure, namely, turbulent and magnetic

pressures.

The clump mean velocities are comparable to the virial velocity, the typical speed

of masses under the influence of gravitational field, and thus, the associated kinetic

energy accounts for a significant fraction of the gravitational energy and contribute

to the support of the cloud against collapse. This internal velocity field, along with

characteristic filamentary structures observed in GMCs, can be explained as a con-

9

sequence of large scale supersonic turbulence. In addition, turbulent motion could

also generate large density fluctuations on small scales, some of which could become

gravitationally unstable and collapse to form stars. Thus, turbulence provides an al-

ternative model to the purely gravitational fragmentation and collapse of clouds into

clumps and cores. Furthermore, it is known that large scale magnetic fields pervade

GMCs and could play a crucial role in the formation and evolution of sub-structures.

Magnetic field can slow or even prevent the gravitational collapse of clouds by a pro-

cess known as ambipolar diÆusion: the ionized fraction of the cloud remains coupled

to the magnetic field and the cloud contraction may be opposed by collisions between

ions and neutrals.

The relative importance of self-gravity, turbulence, and magnetic fields in shaping

the clouds structure is currently a matter of debate (Andre et al. 2008). It is thought

that some combination of these factors govern not only the formation of dense cores

but also their evolution, which could be considered, in some sense, a process analogous

to the evolution of molecular clouds. Once a dense core has been formed, its evolution

depends on the relative strength of the thermal, turbulent, and magnetic pressures

against the cohesive gravitational force.

There are currently two major open issues that still need theoretical and obser-

vational work: (i) whether core formation is initiated by gravitational fragmentation

or turbulent fragmentation; and (ii) whether magnetic fields control core evolution or

not. Several theoretical models have been proposed for the formation and evolution

10

of dense cores in molecular clouds. In summary, we can consider two extreme models

to be compared with observations:

• A slow, quasi-static evolution in which cores form by gravitational fragmenta-

tion in magnetically-supported clouds (Shu et al. 1987; Mouschovias and Ciolek

1999). The gradual core collapse is mediated by magnetic fields and dissipation

of low-level turbulence.

• A dynamic process (known as fast star formation), in which supersonic turbu-

lence creates large density inhomogeneities which may become gravitationally

unstable and collapse to form stars (Ward-Thompson 2002). Magnetic fields

may further contribute to the dissipation of excess turbulent energy.

As we will see later, the observation and characterization of statistically-significant

samples of dense cores is fundamental to constrain the diÆerent star formation models.

1.4 The Observation of Dense Cores

The observational identification of dense cores has been carried out primarily by molec-

ular and millimeter/submillimeter dust continuum surveys. Molecular transitions that

trace relatively high density gas, such as C18O(1–0) and NH3

, provide information

about the dynamics of the system. However, the interpretation of line integrated

intensity maps can be complicated by variations in molecular abundance and chem-

ical evolution within clouds and cores. Moreover, the sensitivity of line observations

11

critically depends on the gas density: a given molecular line is most sensitive above

the critical excitation density and below a saturation limit imposed by the increasing

optical depth. In contrast, millimeter/submillimeter continuum emission maps can

trace the column density (dust content along the line of sight, commonly measured

in units of mass per area) of dense cores without the confusing eÆects of line opacity

and chemical evolution. Therefore, though the estimation of core physical parameters

is still subject to certain assumptions (see section 4.4), dust continuum surveys are

vitally important for probing the nature of these objects. In particular, they represent

the only way of deriving statistically-significant samples of pre-stellar cores.

Dense cores can be separated by observational criteria into three broad categories

(e.g., Di Francesco et al. 2007; Ward-Thompson et al. 2007): starless, pre-stellar,

and proto-stellar cores. Starless cores are defined as concentrations of molecular gas

without any evidence of star formation, such as embedded young stellar objects (YSO),

molecular outflows, or compact radio emission. They are possibly transient objects

supported by external pressure. Pre-stellar cores are the subset of starless cores which

are gravitationally bound and are expected to form stars. Finally, dense cores which

are actually forming stars and are therefore associated with signs of star formation,

are commonly known as proto-stellar cores. Such cores with an embedded YSO can

be viewed actually as the surrounding envelope of an accreting protostar.

In practice, the observational distinction of cores among diÆerent categories is not

generally straightforward, and we note that there is no sharp transition between them,

12

but rather a continuous evolution from pre-stellar to proto-stellar cores. The distinc-

tion between starless and proto-stellar cores is ultimately dependent on the availability

and sensitivity of ancillary data. All-sky IR surveys, traditionally far-infrared (FIR)

data from IRAS (Beichman et al. 1986) and more recently MIR and NIR data from

MSX and 2MASS respectively, have been used to find evidence of embedded YSOs

associated with previously detected cores. However, deep NIR observations towards

specific cores and the high sensitivity of the Spitzer Space Telescope have shown the

inability of previous all-sky surveys to detect the youngest and most embedded proto-

stars. Thus, the ratio of the number of proto-stellar to starless cores usually increases

with the sensitivity of the additional IR data used in the classification. In addition,

the observation of molecular outflows and jets may be used to identify proto-stellar

cores. On the other hand, molecular line profiles can be used to detect inward mo-

tions in cores and therefore distinguish between pre-stellar and starless cores, since

they would provide direct evidence of gravitational binding. An analysis of the core

stability can also be performed using the Bonnor-Ebert criterion (Bonnor 1956), which

will be described later in section 4.5.

The dense, dust cores observed with submillimeter telescopes represent a critical

link between the evolution of GMCs and the formation of stars. The identification

and characterization of these cores provide valuable information about the physical

conditions prior to star formation. Furthermore, comparing the physical parameters

(temperature, mass, luminosity) of starless, pre-stellar, and proto-stellar cores may

13

help to characterize an evolutionary sequence which, in addition, may help to distin-

guish among diÆerent star formation models. In particular, there are some key pa-

rameters which can be directly used to discriminate between the two extreme models

mentioned above: the characteristic timescale of core evolution, the mass distribution

of pre-stellar cores, and the star formation rate and e±ciency.

14

Chapter 2

SUBMILLIMETER OBSERVATIONS AND THE BLASTTELESCOPE

The submillimeter band is the region of the electromagnetic (EM) spectrum lying

between the infrared and microwave bands, and may be defined as the wavelength

range between 200µm and 1000µm. Submillimeter observations of spectral lines and

dust continuum emission contribute significantly to many areas of astronomy, from

planetary science to cosmology. In particular, during the past ten years, the study

of star formation has been revolutionized with the advent of telescopes and cameras

able to map the cold dust component of molecular clouds, providing direct evidence

of dense cores and the youngest protostars. In this chapter, we introduce some basic

concepts in submillimeter astronomy and present a general description of the BLAST

telescope.

2.1 Methods in Submillimeter Astronomy

Despite its clear importance in a wide range of astrophysical contexts, submillimeter

astronomy has been the least explored portion of the EM spectrum. Even today,

the submillimeter sky has not been yet systematically surveyed, because of the high

absorption in the atmosphere at these wavelengths and the technical complexity of the

15

required instrumentation. Between the IR and radio regimes, submillimeter astronomy

makes use of techniques from both IR and radio astronomy.

2.1.1 The Submillimeter Regime

As we have seen in the previous chapter, the very first stages of star formation are

invisible at optical and NIR wavelengths. At the typical densities of pre-stellar cores,

all of the NIR-optical-UV light emitted by the forming star is completely absorbed by

the surrounding dust, which is heated to temperatures ª 10–30K. At these tempera-

tures, the bulk of the emission is radiated in the submillimeter band which, in contrast

to optical and IR wavelengths, remains optically thin even in the densest regions of

molecular clouds. Therefore, submillimeter dust continuum observations can probe

even the center of dense cores, providing a direct measurement of the dust content

along the line of sight (i.e., the column density).

The atmospheric absorption of submillimeter radiation is mainly due to water

vapor and therefore ground-based observatories are built at the highest and driest

possible sites (the Atacama desert in Chile and Antarctica are currently the best places

on Earth). Fig. 2.1 shows a model of atmospheric transmission under good observing

conditions at an altitude of ª 4000m. Ground-based observations at submillimeter

wavelengths are possible only through atmospheric windows of partial transparency

centered on 350, 450, and 850 µm. However, in the wavelength range 200–600 µm

the atmospheric transmission is ∑ 40% even under the best observing conditions.

16

Figure 2.1: Zenith atmospheric transmission at the Caltech Submillimeter Observa-tory (Mauna Kea, Hawaii) for 1mm precipitable H

2

O (Weisstein 1996).

Dust continuum surveys at ∏ 1.2mm suÆer less from atmospheric absorption but

they probe the Rayleigh-Jeans tail of the SED of the dense cores, far from the peak

of the emission. Therefore, millimeter/submillimeters surveys have been considerably

limited in sensitivity and/or spatial coverage to date.

The full submillimeter band can only be observed from space, avoiding the absorp-

tion of the atmosphere. However, the design, construction, and operation of space

telescopes imply huge budgets and long timescales. A cheaper and shorter-timescale

solution can be found in balloon-borne telescopes. Helium-filled balloons of large

volume can lift telescopes above most of the atmosphere, near space-like conditions,

during flights of ∑ 2 weeks duration, called Long Duration Balloon (LDB) flights.

17

2.1.2 Telescope Performance

The telescope performance depends primarily on the diameter of the main reflector

divided by the wavelength of the observed emission, D∏, also known as the electric

diameter. A large electric diameter improves the sensitivity of the telescope (pro-

portional to D2) and its angular resolution (see below). However, a larger electric

diameter demands a better pointing accuracy and a more accurate reflector surface,

which are made di±cult by the eÆects of gravity, wind forces, and temperature gradi-

ents (see Olmi 2003, for a review).

Submillimeter telescopes have the appearance of radio telescopes though some of

them are sheltered by an enclosure similar to optical telescopes (in order to reduce

wind and temperature gradients). Due to the relatively short wavelengths, the pri-

mary mirror must be built to very high accuracy. The design of most submillimeter

telescopes is based on a Cassegrain configuration, in which a hyperbolic secondary

mirror direct the light collected by a parabolic primary mirror through a hole in the

later. In the Ritchey-Chretien version, a hyperbolic primary mirror substitutes the

parabolic mirror in order to correct for spherical aberration at a flat focal plane,

suitable for wide field observations.

In submillimeter telescopes it is very important to block stray radiation, i.e., radi-

ation propagated and/or generated by a non-ideal optical system which does not come

from the object of interest (the field-of-view of the telescope). Stray radiation may be

diÆracted or scattered radiation originated from sources outside the field-of-view and,

18

even more important, thermal emission of objects inside or outside the optical system.

At submillimeter wavelengths, the thermal emission radiated by any component of the

telescope at ambient temperature may contribute to the detectors response far more

than the total radiation coming from an astronomical source. Thus, every component

in the detectors field of view (and the detectors themselves) must be cooled to very

low temperatures (ª 0.3K) for correct operation in the submillimeter regime. The

cryogenic system is therefore an essential element in submillimeter instruments.

The angular resolution (the ability to separate nearby sources) of submillimeter

telescopes is limited by diÆraction (assuming that the image of a point source is

aÆected only by diÆraction and not by geometrical aberrations, or equivalently that

the optical system is “diÆraction-limited”). The beam of the telescope is defined as the

portion of the sky that a single detector in the focal plane can “see”. Therefore, two

adjacent sources falling within the beam of the telescope would be unresolved. Due

to the limited diameter of the telescope, the image of a point source (an unresolved

source whose angular size is smaller than the beam) resembles a typical Airy diÆraction

pattern (see Fig. 2.2). The response of a telescope to a point source, in particular the

x ,y intensity distribution in the image, is called the point spread function (PSF).

A good knowledge of the telescope’s PSF is essential in order to interpret correctly

astronomical images.

19

Figure 2.2: Left panel : DiÆraction pattern produced for light passing through a circu-lar aperture (“Airy pattern”). The first minimum determines the angular resolutionof the instrument: µmin = 1.22 ∏

D, where µmin is the angular displacement from the

centre (expressed in radians), ∏ is the observed wavelength, and D is the diameter ofthe telescope. Right panel : A point source will appear as an extended image with thetotal emission distributed according to the diÆraction pattern and the size of the recon-structed image pixels. Taken from http://laser.physics.sunysb.edu/marissa/report.

2.1.3 Detectors Used in Submillimeter Astronomy

In general, there are two diÆerent types of detectors of EM radiation, which are

related to the wave-particle duality of the EM field. Coherent detectors, commonly

used in radio astronomy, are sensitive to the wave nature of the EM field and detect

fluctuations in the amplitude and phase of the radiation. In addition, they record the

spectral information of the astronomical signal. Therefore, coherent detectors are very

useful to generate interferometric signals1 and obtain the spectrum of astronomical

sources. In contrast, incoherent detectors are sensitive to the particle nature of the

EM field, and can measure only the intensity of the EM radiation which is a measure of

1Interferometry combines the phase information from several receivers on diÆerent telescopes inorder to substantially increase the angular resolution.

20

Figure 2.3: Schematic of a bolometer.

the flux of incoming photons. They are useful for broad-band continuum observations.

Both coherent and incoherent detectors are used at submillimeter wavelengths,

and are known as heterodyne receivers and bolometers, respectively. In the present

work, we are mostly interested in submillimeter dust continuum observations which are

obtained by means of bolometers (Fig. 2.3). These are very sensitive semiconductor

devices which detect EM radiation over a wide spectral range, from millimeter to X-

ray wavelengths. Bolometers consist of an absorber and a thermistor and are made of

diÆerent materials to be optimized for each spectral band. Incoming photons increase

the temperature of the thermistor which results in a change in its electric resistance.

This change in resistance is measured as a change in voltage across the thermistor

which provides, once calibrated, a measurement of the power coming from the detector

field of view at a given instant. The wavelength range of the absorbed photons, the

eÆective bandwidth, is determined by a series of filters placed in front of the detector.

21

Over the past decade, the advent of cameras with many (increasing with time from

a few tens to thousands) individual bolometers operating at the same time, called

bolometer arrays, has significantly increased the mapping capabilities of millimeter

and submillimeter telescopes.

2.1.4 Observables in Submillimeter Astronomy

The basic quantity measured in observational astronomy is the specific intensity I∫ ,

which is the energy per unit time, area, solid angle, and frequency interval emitted or

received in/from a given direction. Thus, the specific intensity, also called brightness

or simply intensity, has units W m°2 Hz°1 srad°1.

When we observe an extended source (i.e., with angular size larger than the beam),

we resolve its intensity distribution. However, observing a point source we can only

measure the flux density, i.e. the total energy received per unit time, area, and

frequency, and we have no information about the spatial distribution of the source.

The flux density, is defined as:

F∫ =

ZI∫ cos µ d≠ , (2.1)

where µ is the angle between the propagation direction and the surface normal,

and d≠ is the diÆerential solid angle. Flux density is, therefore, measured in units

of W m°2 Hz°1 (submillimeter flux densities are usually expressed in Jansky units:

1 Jy = 10°26 W m°2 Hz°1). The flux density received from an extended source may

22

be calculated using equation 2.1, and cos µ = 1 with a very good approximation.

The intensity received from an astronomical source does not depend on the distance

between detectors and source, provided that submillimeter emission is optically thin,

i.e., the extinction by interstellar dust can be neglected. However, the observed flux

density decreases with the square of the source distance. Flux density measurements

over a wide range of frequencies allow the characterization of the energy emitted by

a given source as a function of frequency, the SED, whose shape does not depend on

the distance to the source. If the distance d is known, then, the total energy per unit

time emitted by the source, called luminosity, can be estimated by integrating the

SED over the whole spectral range: L = 4ºd2

RF∫ d∫.

In relatively high density regions such as molecular clouds, the absorption, scat-

tering, and thermal emission of dust grains must be considered to appropriately char-

acterize the radiation field. The equation of radiative transfer describes the change in

intensity with distance, s, along the line of sight:

dI∫

ds= °Ω∑∫I∫ + j∫ , (2.2)

where Ω [g cm°3] is the total density, ∑∫ [cm2 g°1] is the opacity at frequency ∫, and

j∫ is the emissivity of the medium (measured in units of intensity divided by length). If

we consider a cloud in equilibrium at temperature T , the internal radiation field must

be described by the Plank function B∫(T ) everywhere and therefore, from equation 2.2

j∫ = Ω∑∫B∫(T ). Then, in the approximation of Ω, ∑∫ , and T constant across the

23

cloud, and if we consider the dust thermal emission optically thin (appropriate in

the submillimeter regime), the intensity leaving the cloud is I∫ = B∫(T ) ¢ø∫ , where

¢ø∫ = Ω∑∫¢s is the optical depth through the cloud. Finally, if the cloud subtends a

solid angle ¢≠, the received flux would be:

F∫ = B∫(T ) ¢≠ ¢ø∫ . (2.3)

We note that the wavelength dependence of ¢ø∫ is poorly known and thus it is

usually written as a power law, ª ∫Ø. In section 4.4.1 we will use equation 2.3 in order

to model the observed SEDs of the dust cores and obtain their physical parameters.

2.2 The BLAST Telescope

BLAST (Pascale et al. 2008) is a 2-meter telescope designed to perform three-band

photometry at 250, 350, and 500 µm with an in-flight angular resolution of 3600, 4200,

and 6000 respectively. Operated during LDB (Long Duration Balloon) flights above

most of the atmosphere (at an altitude of about 40 km), BLAST achieves the highest

sensitivity in these wavebands obtained to date. This allows a mapping speed approx-

imately an order-of-magnitude faster than any other existing submillimeter facility in

terms of detecting compact sources and even a greater improvement in terms of mea-

suring diÆuse structures in the interstellar medium. The design of BLAST is driven

by its scientific primary goals: (i) to identify and characterize cold dust pre-stellar

cores representing the earliest stage of star formation; (ii) to measure the structure of

24

the interstellar medium and molecular clouds where star formation takes place; (iii)

to constrain the angular and redshift distribution of optically obscured star-forming

galaxies; and (iv) to study the evolution and the spatial clustering of this extragalactic

population.

Until the regular operation of the Herschel Space Observatory, BLAST is unique in

its ability to derive large statistical samples of pre- and proto-stellar cores, providing

the critical spectral coverage needed to constrain column densities, masses, luminosi-

ties and temperatures. In addition, BLAST large-scale surveys enable the study of

the environmental eÆects on the early evolution of star formation, from the scale of

GMCs to individual cold cores. The Galactic and extragalactic maps obtained by

BLAST and the techniques used for map reconstruction and analysis will serve as a

legacy to be followed by Herschel. In this chapter we present a brief description of

the telescope and summarize the observations performed during the 2005 (BLAST05)

and 2006 (BLAST06) LDB flights.

2.2.1 Telescope Design

The BLAST05 telescope had a 2m CFRP (carbon fiber reinforced plastic) spherical

primary mirror and a highly aspherical secondary mirror to compensate the spherical

aberration of the primary (Olmi 2001, 2002). However, the optical design of the

BLAST06 telescope was based on a Ritchey-Chretien configuration. The radiation

collected by the warm optics of the telescope is redirected to a cryostat where cold

25

Frequency (Hz)600 800 1000 1200 1400 1600

-20

-15

-10

-5

0

-20

-15

-10

-5

0

Rel

ativ

e R

espon

se (

db)

Figure 2.4: Relative spectral response of the three BLAST broadband filters in the2005 (top panel) and 2006 (bottom panel) configurations (Pascale et al. 2008).

reimaging optics (three additional mirrors) corrects the sky image for aberrations. A

set of filters placed inside the cryostat block IR emission, split radiation into three

beams (one for each wavelength; see Fig. 2.4), and define the wide band seen by each

detector (ª 30% bandwidth). BLAST makes use of almost the same bolometer arrays

as the SPIRE instrument on Herschel, consisting of 149, 88, and, 43 elements at 250,

350, and 500µm respectively, organized in a hexagonal pattern.

A rigid and relatively light aluminum structure, the gondola frame, supports all

the elements of the telescope. The gondola consists of two main components, an

outer frame which provides the attachment point to the balloon flight train, and an

inner frame attached to the outer frame along a horizontal axis and free to move in

elevation from 25± to 60± with respect to the outer frame. Fig. 2.5 shows a model of

26

Figure 2.5: Model of the BLAST gondola in the 2006 configuration (Pascale et al.2008).

the telescope and gondola in the 2006 configuration. Sun shields are used to protect

the telescope from solar radiation during the day and a set of solar panels provides

the power required by the electronics, estimated to be ª 650W.

BLAST can be controlled from the ground station only while the gondola is within

line-of-sight and thus, it must be fully autonomous. Two redundant flight computers

receive and process information from all systems in the telescope, control the gondola

motion, and write the data to disk. BLAST is primary driven by three torque motors

which provide the active pointing of the telescope (see Fig. 2.6). Placed in the outer

frame, a high-moment of inertia reaction wheel controls the azimuth pointing by

rotating the gondola against it (due to angular momentum conservation). A pivot

27

motor at the attachment point to the flight train prevents the reaction wheel from high

speeds, counteracting torques from the balloon and diÆerential wind speeds. Pointing

control in elevation is provided by a torque motor mounted at the attachment point

of the inner frame to the outer frame.

In order to produce fully sampled images and modulate the bolometer signals

the telescope must scan each field several times. Cross-linking (scanning in diÆerent

directions) is also necessary to reduce large scale noise in the map. We note that a

balloon-borne telescope experiences a very complex motion during its flight, travelling

free in latitude and longitude, undergoing rotations and oscillations due to variable

torques. Thus, accurate pointing sensors and responsive motors are required, as well

as an automated flight scheduler which provides a list of observations or actions in an

optimal way according to the desire list of targets, scan parameters, and observational

constrains at each moment. The primary pointing sensors are a set of gyroscopes

and two optical star cameras which are complemented by secondary sensors as a

magnetometer, Sun sensor, and a diÆerential GPS. Gyroscopes measure the telescope’s

angular velocities about three orthogonal axes, while star cameras are necessary to

calculate its absolute orientation (by comparing the relative position of three stars,

at least, to the position of stars in a reference catalog). The telescope’s orientation

on the sky as a function of time, the pointing solution, is calculated in-flight with an

accuracy of ª 3000.

28

Figure 2.6: Front and side schematic drawings of the BLAST gondola (Pascale et al.2008).

2.2.2 Data Reduction: Calibration, Pointing Solution, and Map Making

BLAST data reduction is a complex process involving many steps before making a

final map of the sky specific intensity. In general terms, time-varying bolometer data

must be calibrated, combined with a post-flight pointing solution, and projected into

the associated map pixels (for a complete description of the process, see Pascale et al.

2008; Patanchon et al. 2008; Truch et al. 2008, 2009).

First of all, bolometer time streams, in voltage units and sampled at 100Hz, must

be cleaned of spikes from cosmic-ray hits and deconvolved from the filters applied by

the electronics. Bolometer responsivities (change in voltage due to a change in incident

power) fluctuate across the flight and therefore a correction must be considered. An

internal calibration lamp provides a stable signal used to track these fluctuations

and generate a constant responsivity time stream for each bolometer, which can be

29

combined with the pointing solution to make individual maps.

Post-flight pointing reconstruction provides an absolute pointing of the telescope

with an uncertainty ∑ 500. Gyroscope rates are integrated from the absolute reference

provided by the star camera (given every two seconds) and sampled in phase with the

bolometer time streams. A rotation must be applied to change from the star camera

reference frame to the bolometer array coordinate frame and a correction accounts

for the relative alignment between the star camera and the telescope. Thus, for each

bolometer, the voltage “coming” from some specific sky coordinates is known as a

function of time.

In order to make a map of the sky, the region of interest is conveniently divided

in pixels so that each data sample can be assigned to the map pixel to which the

detector points at each time interval. Pixel size must be smaller than the telescope’s

PSF in order to avoid loss of information. Initial pixel values, still in voltage units,

are calculated as the average of all data samples corresponding to each pixel. It is

important to note that the final map at each wavelength will be the result of the

convolution of the “true” sky emission with the telescope’s PSF and then, integrated

over the normalized band-pass filter.

Before averaging data samples from diÆerent detectors, bolometer global respon-

sivities must be compared to a reference bolometer for each wavelength. The rela-

tive calibration of diÆerent detectors, known as flat-fielding in optical astronomy, is

performed by comparing the total integrated flux density from a point source, indi-

30

vidually calculated from maps made for each bolometer. For that purpose, a bright

and point-like calibration source is mapped with the slow scan velocity needed to be

fully-sampled from single bolometer data.

Now, constant and calibrated responsivity time streams can be used to make high

signal-to-noise ratio multibolometer maps. However, the large number of detectors

used by BLAST in addition to the on-axis design results in a significant eÆect of

the correlation between time streams. Correlated noise, unlike uncorrelated noise,

does not integrate down while increasing the number of detectors; therefore, diÆerent

time streams cannot be considered as independent data in the mapmaking process.

To address this problem, which is common to all BLAST data, a new mapmaking

method was developed: Signal and Noise Estimation Procedure Including Correlations

(SANEPIC), which is fully explained in Patanchon et al. (2008).

Finally, multibolometer maps, in voltage units, must be converted into flux-unit

(or intensity) calibrated maps which are then used in all the subsequent analysis.

Astronomical flux calibration is performed by comparing the band-averaged flux of

an isolated, point-like source as obtained from the BLAST maps to the value pre-

dicted from its SED (Fig 2.7). Thus, the primary calibration source must have a

well-constrained SED from millimeter to FIR wavelengths and must be accessible

throughout the flight. Available photometry is used to construct the SED and then

fitted to an isothermal modified blackbody model (see section 4.4 for a description

of the model and the fitting procedure). Then, predicted band-averaged fluxes are

31

Figure 2.7: SED of the absolute flux calibrator for BLAST06 (the red hypergiantstar VYCMa), as determined by published data from IRAS, SCUBA, SHARC-II, andBolocam. The black line represents the best-fit model and the grey lines show the 68%confidence intervals. Black diamonds indicate the model predictions at 250, 350, and500µmBLAST broad-band filters (normalized to an arbitrary amplitude) are shownfor reference (Truch et al. 2009).

Figure 2.8: BLAST06 PSFs at 250, 350, and 500 µm obtained from observations ofVYCMa. The open circles represent the expected diÆraction limited full width at halfmaximum (FWHM) for each waveband (Truch et al. 2009).

32

calculated by integrating the SED model over the normalized band-pass filters (see

equation 4.7). The calibration coe±cient is calculated as the ratio of the BLAST

fluxes to those predicted and it is finally applied to the maps. We note that BLAST is

insensitive to absolute brightness and the final maps have an arbitrary mean (the DC

level is unconstrained). Therefore, background subtraction is necessary to perform

any flux density measurement.

2.2.3 BLAST 2005 and 2006 Science Flights

BLAST has made one test flight and two LDB science flights to date. A first 1-day

flight was devoted to test the instrument performance in order to ensure a proper

operation during the following flights. In 2005, a 5-day science flight was conducted

from Sweden to northern Canada. Extragalactic observations could not be performed

due to a failure in the optical system (with a significant degradation of the beam

shape) and thus, the full observing time was spent in Galactic surveys for which

the data are still very relevant. The second science flight took place in 2006 from

Antarctica. During the 11-day flight the telescope performed successfully, achieving

the main Galactic and extragalactic goals.

BLAST surveyed a total of ª 20 deg2 of the Galactic plane visible from the north-

ern hemisphere during its 2005 LDB flight, including extensive maps toward Vulpecula,

Cygnus, Aquila, and Sagitta, well-known sites of high-mass star formation. A detailed

study of the 4 deg2 Vulpecula map has been published recently (Chapin et al. 2008),

33

and the other regions are currently under analysis. During the 2006 LDB flight,

BLAST surveyed ª 200 deg2 of the southern Galactic plane at nearly diÆraction-

limited resolution. The surveyed area includes a ª 50 deg2 deep map of the Vela

Molecular Ridge nested inside a wider and shallower map, ª 3 deg2 in Eta Carina,

ª 1 deg2 map of the Gum nebula, and a ª 3 deg2 map towards the supernova remnant

Pup A. In addition, during this flight BLAST conducted unprecedented extragalactic

surveys towards the GOODS South field and the South Ecliptic Pole. First Galactic

and extragalactic results have been published recently and may be accessed from the

BLAST web page2.

2http://blastexperiment.info/results.shtml

34

Chapter 3

OBSERVATIONS

In this chapter we introduce the Vela Molecular Ridge (VMR), a region exten-

sively mapped by BLAST in the submillimeter dust continuum emission. We briefly

describe the current understanding of star formation, based on previous observations

by diÆerent authors, and introduce the region analyzed in the present work, the Vela-

D cloud. Then, the specific observations performed towards this cloud at diÆerent

wavelengths are described in detail, with special attention to the maps and catalogs

used in this work.

3.1 The Vela Molecular Ridge

The VMR is one of the nearest GMC complexes. Located in the Galactic plane (b

= ± 3±) and extended over 17± in longitude (l ª 257±–274±), it was firstly observed

by May et al. (1988) in the 12CO(1–0) transition. Murphy and May (1991) subdivided

the region into four main GMCs, named A, B, C, and D, and estimated their 12CO

masses to be ª 105 MØ (see Fig. 3.1). They found that most of the molecular gas

content is located behind the Vela supernova remnant (SNR; estimated to be at a

distance of ª 500 pc) and noted that the CO emission is well correlated with optical

HII regions but uncorrelated with OB stars observed in the same field.

35

Figure 3.1: 12CO integrated intensity map of the VMR (Murphy and May 1991).

Liseau et al. (1992) went over distance evaluations and estimated that GMCs A,

C, and D are located at a distance of 700± 200 pc while GMC-B is ª 2 kpc away. A

more detailed analysis of the dense molecular gas in the VMR was carried out by Yam-

aguchi et al. (1999) in the 12CO, 13CO, and C18O (1–0) emission lines. They resolved

GMCs C and D into smaller clouds and clumps and showed that GMC-C is more

massive and probably in an earlier evolutionary state than GMC-D. Higher resolution

12CO observations of Moriguchi et al. (2001) showed a general correlation between

the CO emission and the X-ray distribution from the Vela SNR, suggesting a possible

interaction between molecular clouds and the supernova remnant (see Fig. 3.2).

Through a number of continuum and spectral line observations, the VMR appears

as an actively low- and intermediate-mass star forming region. It has been searched

for YSOs by diÆerent authors, usually through the IRAS and/or MSX Point Source

Catalogues (PSC). NIR observations of selected IRAS sources performed by Liseau

36

Figure 3.2: 12CO integrated intensity map of the VMR (contours) overlayed to theX-ray image of the Vela SNR (grey scale) (Moriguchi et al. 2001).

et al. (1992) and Lorenzetti et al. (1993) led to the first catalogue of Class I objects

in the VMR. They found that low- and intermediate-mass star formation has been

occurring for at least 106 years, as suggested by the presence of Class II sources. Based

on bolometric luminosity estimations, they concluded that there is no current massive

star formation in the region, at least in GMCs A, C, and D.

Subsequent NIR observations towards GMCs C and D by Massi et al. (2000, 2003)

showed that IRAS sources with bolometric luminosities Lbol

> 103 LØ are in fact

associated with young star clusters (< 100 members) which present a stellar mass dis-

tribution similar to the canonical initial mass function (IMF). They found that Class

37

I-consistent IRAS sources are the most massive and less evolved components in each

cluster. In addition, the presence of IRAS sources with low bolometric luminosities,

associated with small groups of YSOs or isolated, suggests that both cluster and dif-

fuse star formation modes are present in the GMC-D. Furthermore, protostellar jets

found by Lorenzetti et al. (2002) and De Luca et al. (2007) using NIR imaging and

spectroscopy evidenced a high activity in the region, supporting also the picture of

diÆerent modes of star formation. The interaction between some of these protostellar

jets and the surrounding medium has been studied in great detail by Giannini et al.

(2005).

New 12CO(1–0) and 13CO(2–1) arcminute-resolution observations performed by Elia

et al. (2007) highlighted the complex dynamics dominating the GMC-D. They found

diÆerent structures such as clumps and filaments associated with FIR sources and

suggested the existence of expanding shells which could be the cause of the observed

cluster star formation. Expanding shells appear as arc-like and elliptical emitting

regions using integrated intensity maps and velocity field analysis. Their origin was

discussed in terms of possible interactions between the molecular cloud and near ex-

panding HII regions, stellar winds from a previous generation of young stars, and

even the Vela SNR. However, none of these descriptions seemed to be convincing and

the hypothesis of filamentary structures being a natural consequence of turbulent gas

evolution cannot be rejected. In addition, Elia et al. (2007) identified a number of

molecular outflows originated in the vicinity of the IRAS sources.

38

FIR sources were also found to be associated with dense dust cores as shown

by Massi et al. (2007) from 1.2mm dust continuum observations, confirming that

they are in an early evolutionary state. De Luca et al. (2007) compiled a list of ª 60

dust cores and identified their IR counterparts using the IRAS, MSX, and 2MASS

catalogs in addition to further NIR data. They found that the majority of the core

counterparts have SEDs consistent with being Class I protostars and about one third

of the cores present jets driven by sources located into their interior. The preceding

description of the GMC-D young stellar population was significantly improved by

means of a recent Spitzer MIPS imaging survey carry out by Giannini et al. (2007).

They considerably increased the number of known YSOs, identified some of the sources

driven protostellar jets, and proposed some MIPS sources as potential candidates for

Class 0 objects.

The millimeter dust continuum survey performed by Massi et al. (2007) was not

sensitive enough to obtain a complete census of pre- and proto-stellar cores in the

GMC-D region, essential for understanding the very early stages of star formation. In

a very recent submillimeter continuum survey presented in Netterfield et al. (2009),

BLAST has characterized the cold dust emission in the VMR in a wide scale range,

from GMCs to individual cores (see Fig. 3.3). They have identified over a thousand

compact sources simultaneously in the three bands. Through the BLAST three-band

photometry alone, they have estimated the temperature, mass, and luminosity of the

dense cores, characterizing the youngest population in the GMC complex. In addition,

39

using the source temperature to discriminate between starless and proto-stellar cores

(they considered a separation limit of 14K based on correlations with the IRAS and

MSX PSCs) they inferred a mass dependent cold core lifetime significantly longer

than free fall and turbulent decay models, suggesting the existence of an additional

non-thermal support.

However, though BLAST bands sample the peak of the SED of the coldest objects,

temperature and therefore, mass and luminosity estimations of warmer cores are sub-

ject to significant uncertainties. Therefore, additional FIR-MIR data are needed to

better constrain the dense core physical parameters. Furthermore, MIR/NIR data are

essential to directly discriminate between starless and proto-stellar cores, without the

need of assumptions about the temperature of the cores. In this work, we analyze in

detail the portion of the GMC-D encompassing the majority of the millimeter to NIR

data mentioned above, in order to carry out a detailed multi-wavelength analysis of

the BLAST cores. Thus, for practical reasons we limit our analysis to the area con-

tained within 262±.80 < l < 264±.60 and °1±.15 < b < 1±.10, which we refer hereafter

as Vela-D.

40

3.2 BLAST Data

3.2.1 BLAST Observations

BLAST mapped ª 50 deg2 of the VMR in a 21-hour survey performed during the 2006

science flight. Fig. 3.3 shows a composite RGB image1 made from the BLAST 250,

350, and 500µm maps, where color scale is an indicative of temperature: blue and red

colors indicate warmer and cooler regions respectively. Spanning ª 10± in Galactic

longitude and ª 5± in Galactic latitude, BLAST maps encompas the main four GMCs

previously detected in molecular line observations (Murphy and May 1991). Dust

emission follows a similar distribution to the gas traced by the 12CO(1–0) transition

(see Fig. 3.1 and Fig. 3.2). A large number of diÆerent structures such as clumps,

filaments, and individual dense cores are observed across the field. Netterfield et al.

(2009) have identified and measured the flux densities of 1282 sources simultaneously

at 250, 350, and 500µm. The sample of 141 BLAST sources located within Vela-D

(see Fig. 3.3 and Fig. 3.4) will be used as a reference catalog for all the subsequent

analysis.

1False color image in which the color of individual pixels is specified by three values, indicating aparticular combination of red, green, and blue colors.

41

Fig

ure

3.3:

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42

3.2.2 Source Identification and Flux Extraction in the BLAST Maps

The source extraction technique applied to the Vela map is similar to the method

described by Chapin et al. (2008) for the analysis of the BLAST-2005 Vulpecula

maps. After a MHW (mexican hat wavelet; i.e., the negative normalized second

derivative of a Gaussian function) type convolution is applied to the 250 and 350 µm

maps, intensity peaks above a certain threshold are identified as candidate sources (the

500µm map is not used for source identification due to its intrinsic lower resolution

which results in higher source-source and source-background confusion). Then, 2-

dimensional Gaussians are fitted to each candidate source, determining their FWHMs

and centroid positions, and the 250 and 350 µm candidate lists are merged to create

a final source list containing positions and sizes. Finally, flux densities are calculated

as integrals of Gaussians of fixed size and position and variable amplitude fitted to

the 250, 350, and 500 µm maps. The bias and completeness of the source and flux

extraction method is evaluated using Monte Carlo simulations (see Fig. 3.5). Fake

sources (Gaussians of known amplitude, size, and position) are randomly added to

the maps and the source extraction pipeline is applied again. The comparison of the

input to the output values allows quantifying the completeness and determining a flux

correction factor as the mean ratio of the input simulated flux to the recovered flux.

Once local emission peaks have been identified, flux extraction could be performed

using aperture photometry or PSF photometry instead of Gaussian photometry. We

have applied and compared these methods in the Vela-D map. In summary we found

43

Figure 3.4: Colour-scale image of the BLAST 250 µm Vela-D map, with the locationsof compact sources indicated by circles of diameter proportional to the observed sourcesizes. Dust cores are usually found in the densest regions of the cloud, often associatedto shell-like structures and filaments.

44

that: (i) aperture photometry is seriously aÆected by contamination in crowded fields

and therefore cannot be generally applied to the BLAST maps; (ii) PSF photometry

is very e±cient in crowded fields but provides accurate fluxes only for point sources;

and (iii) multi-Gaussian photometry can deal with both crowded fields and resolved

sources.

In addition, Gaussian fitting provides us information about the source size. The

high signal-to-noise ratio of the BLAST maps allows us to infer the intrinsic size of

the BLAST sources using the standard Gaussian deconvolution formula:

µdec

= (µ2

fwhm

° µ2

beam

)1/2 , (3.1)

where µfwhm

is the observed FWHM from the best Gaussian fit and µbeam

is the

BLAST beam size. The FWHM median value in the VMR is 6200, which corresponds to

an intrinsic deconvolved size of 0.15 pc at a distance of 700 pc (the estimated distance

of the GMCs A, C, and D).

BLAST has a beam width of 3600 at 250µm (i.e eÆective spatial resolution of

ª 0.1 pc) and therefore it seems that BLAST sources are individual resolved objects.

This spatial resolution is comparable to the average size of the dense cores found by

Motte et al. (2007) in Cygnus-X (with a resolution of ª 1100 using MAMBO at IRAM )

but considerably larger than values reported by Ward-Thompson et al. (1999) and

Enoch et al. (2008) in nearby star forming regions. Therefore, we cannot exclude that

some of the BLAST cores may be actually composed of multiple objects. Furthermore,

45

Com

ple

ten

ess

0.2

0.4

0.6

0.8

1

Source FWHM ["]

Nu

mber

in

Bin

40 50 60 70 80 90 1000

10

20

30

40

50

60

Figure 3.5: Top panel. Completeness of the source extraction method as a function ofsize. Bottom panel. Size distribution of the BLAST cores in the VMR. The blue barscorrect for the size completeness shown in the top panel. (Netterfield et al. 2009).

it has been shown that the ratio µdec

/µbeam

is independent of the spatial resolution in

star forming regions, and it seems to be a consequence of fitting Gaussians to power-

law structures convolved with the beam. Dust cores appear as extended sources of

power-law radial profiles immersed on a medium populated with structures of a wide

range of scales. Therefore, they cannot be described as isolated objects with well

defined boundaries. The size of the dust cores is not a well-defined concept and

should be regarded as a typical scale rather than an absolute physical parameter.

3.3 Archive Data

3.3.1 SIMBA

A ª 1 deg2 area of Vela-D was mapped in the 1.2 millimeter continuum using the

bolometer array SIMBA at the SEST (Massi et al. 2007). The 1.2mm map (see

46

Fig. 3.6) has a resolution of ª 2400 (the SIMBA beam at this wavelength), and the

r.m.s is ª 20mJy/beam with significant variations over the map. The dust emission

was analyzed using the CLUMPFIND algorithm which allows recognizing compact

condensations with arbitrary shapes. From the CLUMPFIND output, Massi et al.

(2007) reported a robust list of 29 dust cores with sizes greater than the SIMBA beam

(“MMS” cores following their definition) and a sample of 26 candidate cores with sizes

smaller than the SIMBA beam (“umms” cores) and therefore initially discarded. The

deconvolved sizes of the MMS cores are in the range ª 0.03–0.25 pc (at a distance of

700 pc) and have masses in the 0.4–88MØ range (estimated by assuming a temperature

of 30K, a dust-to-mass ratio of 100, and a dust opacity ∑1.2 = 0.5 cm2 g°1 at 1.2 mm).

The umms sources were studied in detail by De Luca et al. (2007) and they found that

many of them show signposts of star formation (associations with IR sources and/or

protostellar jets), suggesting that these objects are probably real millimeter sources

and not just instrumental artifacts. Therefore, we consider the complete SIMBA

catalog, MMS and umms cores, to look for possible associations with the BLAST

cores.

An immediate comparison between the SIMBA and BLAST 250 µm maps (see

Fig. 3.4 and Fig. 3.6) reveals a good correspondence between them. However, though

both wavelengths are supposed to trace basically the same neutral material (cold dust

grains), we can clearly see that most of the extended emission is not detected by

SIMBA, due to its lower sensitivity compared to BLAST. The bulk of the SIMBA

47

Figure 3.6: SIMBA 1.2mm emission overlaid with the BLAST 250 µm contours at100, 300, and 1000MJy/srad. The position of the BLAST sources are indicated usingwhite crosses. Also shown are the IRS sources from Liseau et al. (1992).

48

1.2mm emission essentially originates from known sites of recent star formation dis-

covered by IRAS. In fact, a significant fraction of the SIMBA cores, the most massive

and luminous, are associated with IRAS sources, denoted as IRS by Liseau et al.

(1992) when associated with NIR objects. The brightest IRS sources are associated

with young stellar clusters (Massi et al. 2007).

3.3.2 IRAS, AKARI, and MSX

During a period of ten months in 1983, the Infrared Astronomical Satellite (IRAS )

performed an all sky survey at 12, 25, 60, and 100 µm with an angular resolution

of approximately 0.5, 0.5, 1.0, and 2.0 arcmin, respectively. In this work we use the

IRAS PSC version 2.0 (Beichman et al. 1988) to obtain flux measurements of the

BLAST cores at 60 and 100µm. This catalog provides positions, flux densities, and

uncertainties of ª 250 000 point sources with completeness limits of about 0.4, 0.5,

0.6, and 1.0 Jy at 12, 25, 60, and 100 µm. We find 157 IRAS PSC sources within

Vela-D. In addition, we use the IRAS Galaxy Atlas2 (IGA), a reprocessed version of

the maps at 60 and 100 µm, to complement the PSC photometry. These maps are

produced using the resolution-enhancing algorithm HIRES (Aumann et al. 1990) and

the PSFs vary across the field, showing strong elongation along the scan direction.

The Far-Infrared Surveyor (FIS) on the AKARI space telescope (Yamamura 2008)

is an instrument designed to perform an all-sky survey at 60, 90, 140, and 160 µm with

2http://irsa.ipac.caltech.edu/data/IGA.

49

much better sensitivity and spatial resolution than IRAS. The AKARI data should be

very useful to characterize the FIR emission of the dense cores observed by BLAST. In

this work we use a preliminary version of the AKARI source catalog to find the FIR

counterparts of the BLAST cores. In addition, we make use of the MIR all-sky survey

performed by the Midcourse Space Experiment3 (MSX ) at wavelengths 8.28, 12.13,

14.65, and 21.3µm. Though this survey is much less sensitive than the observations

performed by the Spitzer MIPS and IRAC instruments (see below), the MSX PSC

is valuable to find MIR counterparts for the BLAST cores located outside the area

covered by MIPS and IRAC. We find a total of 362 AKARI sources and 194 MSX

sources within Vela-D.

3.3.3 MIPS

Our multi-wavelength analysis takes advantage of the Spitzer MIPS imaging survey

presented in Giannini et al. (2007). They mapped an area of ª 1.5 deg2 at 24, 70, and

160µm, although the coverage of the latter was incomplete due to the very small field

of view of the 160µm array (the mapping parameters were optimized for the 24 and

70µm bands). Therefore, only the 24 and 70 µm maps were exploited for scientific

analysis (see Fig. 3.7). Final calibrated and mosaiced images were obtained using the

standard MOPEX package4. Though the main instrumental artefacts were removed

from the mosaiced images, some residual stripes oriented in the scan direction are

3http://irsa.ipac.caltech.edu/Missions/msx.html.4Provided by the Spitzer Science Center.

50

quite evident in the 70µm map, aÆecting significantly the point source extraction and

photometry at this wavelength.

Giannini et al. (2007) compiled a catalog of 849 and 61 point sources detected at

24 and 70µm respectively. Spitzer is diÆraction limited in the MIPS bands with a

spatial resolution of ª 600 at 24µm and ª 1800 at 70µm and the survey was complete

down to 5 mJy and 250mJy respectively. The point source extraction and photometry

were performed using the standard astronomical package IRAF. Peaks were identified

in a diÆerential image produced by subtracting a “sky” image from the final mosaic,

and flux densities were measured using aperture photometry. After applying a spatial

filter to have a more uniform sensitivity level across the field, they performed PSF

photometry by fitting an empirical point response function derived from a sample of

“bona fide” point sources in the same image. This approach allowed them to obtain

an enlarged catalog of 1347 and 63 point sources at 24 and 70 µm respectively with

better photometric accuracy.

3.3.4 IRAC

An area of ª 1 deg2 has been recently mapped by means of Spitzer -IRAC observations.

Though this survey has not being published yet (Strafella et al. 2009), we use the IRAC

data to characterize the BLAST sources up to the shortest possible wavelengths. The

IRAC sensitivity allow us to detect objects with fluxes down to 50 µJy, reaching a

completeness limit of ª 400 µJy. As for the MIPS data, the IRAC maps have been

51

Figure 3.7: False colour image of the Vela-D map using the MIPS 24 µm channel forblue, the MIPS 70µm channel for green, and the BLAST 250 µm channel for red. Theregions observed by the diÆerent instruments are shown for reference: green and bluelines show the MIPS 70 and 24 µm coverages respectively, the purple line encloses theregion covered by IRAC, and the contours represent the SIMBA map.

52

analyzed using the DAOPHOT task of the IRAF package and PSF photometry was

performed instead of aperture photometry. The final point source catalog contains

the position and fluxes of more than 170 000 sources detected above 5æ at least in one

of the IRAC bands, 3.6, 4.5, 5.8, and 8.0µm.

The high sensitivity of the IRAC image (see Fig. 3.8) allows us to see a general

correspondence between the emission at 8µm and 250µm as partially expected be-

cause both trace neutral material. However, the 8 µm emission is known to be heavily

influenced by PAH (polycylic aromatic hydrocarbon) emission and therefore is not

proportional to the column density. In addition, we found that some of the BLAST

sources are coincident with local minima or darker regions in the IRAC image, sug-

gesting the existence of the so-called Infrared Dark Clouds (see, e.g., Egan et al.

1998).

53

Figure 3.8: Overlay of the IRAC 8 µm image (color scale) with the BLAST 250 µmcontours. The position of the BLAST sources are indicated using white crosses.

54

Chapter 4

ANALYSIS

The primary goal of this work is to characterize the SED of the dust cores found

by BLAST from millimeter to MIR wavelengths and then analyze their physical prop-

erties. For that purpose, we need: (i) to construct the SEDs of the BLAST sources

and thus to associate the flux densities measured by BLAST at 250, 350, and 500 µm

with the emission observed at other wavelengths; and (ii) to fit these SEDs assuming

a theoretical model and relate the fitting parameters to the core physical properties.

4.1 Overview of the Analysis

In order to construct the SED of the BLAST cores we have essentially two possibilities:

• To look for candidate counterparts from catalogs at diÆerent wavelengths, adopt-

ing the flux densities of the sources associated with the BLAST cores.

• To estimate flux measurements at diÆerent wavelengths directly from the original

maps, performing our own source photometry.

In practical terms, we often need to use both methods depending on the available

data, catalogs and/or images, at diÆerent wavelengths. However, care must be taken

in determining the correct set of flux densities which appropriately describes the SED

55

of the BLAST cores. Generating a source catalog is a complex process which usually

involves the use of source and flux extraction methods specifically dedicated and de-

veloped according to the particular characteristics of the data. Therefore, as a first

attempt to characterize the SED of the BLAST cores, we initially used the available

catalogs at diÆerent wavelengths. However, source and flux extraction techniques are

also dependent on the specific goals of the analysis. We must, thus, define the object

under analysis and decide about the correct flux extraction method to be used at

diÆerent wavelengths.

4.1.1 What are the Correct Measurements?

We are interested in the study of the earliest stages of star formation, which are closely

related to the evolution of dense dust cores. Therefore, our analysis is focused on the

submillimeter dust cores found by BLAST and how they emit radiation at other

wavelengths. As we have seen, BLAST sources are defined by Gaussian fits which

delimit the specific areas of the sky from where flux densities are extracted. Thus,

in determining the SED of a BLAST core, we should ideally integrate the specific

intensity emitted at other wavelengths over the same area of the sky, even though this

approach still does not ensure that the integrated emission in each waveband comes

from the same volume of material (see below).

Following this idea, a rigorous flux extraction method would be to convolve the

maps (at diÆerent wavelengths) to the BLAST resolution and then to fit the Gaussian

56

profiles extracted from the BLAST maps, with fixed sizes and positions. Then, the

integral of these Gaussians would provide the flux densities associated to the BLAST

cores. Alternatively, we could use the original maps at native resolution, fitting di-

rectly the BLAST Gaussian profiles convolved to account for the diÆerent beam sizes

at diÆerent wavelengths. This is essentially the method applied to the BLAST maps

where the final fits are performed using the same Gaussian profiles at the three wave-

bands. However, at shorter wavelengths the brightness distribution is quite diÆerent

compared to the BLAST emission (see Fig. 5.1 and Fig. 5.2) and therefore this method

cannot be generally applied.

Aperture photometry allows to integrate the emission over any desired aperture.

Selecting a dynamical aperture of radius Rdyn

= 1.3 £ FWHM, centered over the

BLAST coordinates, the resulting flux density would be equivalent to 95% of the

integral of a Gaussian of a given FWHM. However, the typical large apertures needed

for BLAST cores often result in a significant contamination from nearby sources and

diÆuse emission due to the characteristic crowding in GMCs. The question whether

or not this “contamination” at diÆerent wavelengths is actually associated with the

BLAST core emission must be addressed in a case by case basis. We also note that

integrating the emission in a dynamic aperture can often lead to the estimate of only an

upper limit. Thus, in order to constrain the SED of the BLAST cores more eÆectively,

we need to analyze in detail each case individually, often through the visual inspection

of the same fields at diÆerent wavelengths.

57

Submillimeter dust cores present density and temperature gradients, particularly

proto-stellar cores, implying that the bulk of the emission at diÆerent wavelengths

may come from diÆerent volumes of material. In addition, the optical depth depends

on the observed wavelength (increasing towards shorter wavelengths) and the instru-

mental response is also diÆerent. It is thus not possible to define a “unique core”

at diÆerent wavelengths or even state that “the same core” is seen at diÆerent wave-

lengths. Theory and observations suggest that proto-stellar cores (with a protostar

or proto-stellar cluster) are likely composed of a warmer central part embedded in

a colder and less dense envelope1. At wavelengths shortward of ª 100µm the bulk

of the emission comes from the inner part and the contribution from the envelope

can be neglected. Therefore, integrating over a reasonable small aperture we can ap-

proximately measure the flux density emitted by the system at a given wavelength,

avoiding major contamination problems. In the MIR regime we can even consider

that the bulk of the emission comes from point sources inside the BLAST cores and

thus perform PSF photometry, which is the most accurate flux extraction method in

crowded fields. However, PSF photometry may underestimate flux densities in the

case of compact or extended sources.

1Many Starless cores are thought to be cooler inside and warmer outside due to the absence ofan inner YSO and the presence of an external heating source, the interstellar radiation field.

58

4.1.2 The Approach

In summary, we have seen that submillimeter dust cores are very complex systems and

there is no universally valid flux extraction method. Therefore, in order to construct

the SEDs, we perform the following steps:

• execute the cross-correlation between the BLAST and ancillary catalogs.

• obtain additional photometry for comparison with catalog fluxes and estimation

of upper limits.

• create multi-wavelength images, or “thumbnails”, showing the position of cat-

alog sources, the size of BLAST cores, and the apertures used for aperture

photometry.

• fit preliminary SEDs and visually analyze each source, using the thumbnails as

a qualitative reference.

• fit final SEDs using the appropriate fluxes.

For those cases where catalog fluxes need to be revised, we perform specific aperture

photometry centered over the coordinates of the catalog sources. In addition, for

those BLAST sources without counterparts at a given wavelength, we estimate upper

limits using aperture photometry centered over the BLAST coordinates (except at

wavelengths ∑ 24µm, see section 4.4). The thumbnails allow us to visually assess

59

the presence of multiple sources, as well as other morphological characteristics (e.g.,

a “core +halo” structure), in the diÆerent wavebands. An additional comparison can

be made with the preliminary SEDs obtained using only BLAST flux densities.

4.2 1200–8 µm Source Identification

In order to find candidate counterparts of the BLAST cores we initially adopt a spatial

criterion and perform cross-correlations with all the existing catalogs. We consider

a source being associated with a BLAST core when their distance is less than the

following dynamical search radius:

Rsearch

= [(≤blast

)2 + (≤archive

)2]1/2 (4.1)

where ≤archive

is the pointing error associated with the position of the archive source,

and we conservatively define a positional uncertainty of the BLAST source:

≤blast

= [(FWHMdec

/2)2 + (≤ptg

)2 + ≤2

extr

]1/2 (4.2)

Where FWHMdec

is the deconvolved FWHM of the BLAST source, ≤ptg

is the

BLAST pointing error and ≤extr

is the uncertainty in the source position due to the

source finding technique (both estimated to be ' 500). This search criterion takes into

consideration that BLAST cores are extended sources which may be associated with a

number of point-like sources. In fact, we have found that some BLAST cores appear

to be associated with NIR clusters.

60

4.2.1 SIMBA

The higher signal-to-noise ratio of the BLAST maps compared to the SIMBA map is

evident in terms of the number and distribution of dust cores detected in the same

region. As we have seen in Fig. 3.6, most of the SIMBA emission seems to be associated

with a BLAST core. In order to perform a cross-correlation between the BLAST and

SIMBA catalogs we assume ≤simba

= 500, the estimated SIMBA pointing error (Massi

et al. 2007). After applying our search criteria, we find that 31 BLAST cores have one

or more SIMBA counterparts, corresponding to ª 36% of the BLAST sources located

within the area observed by SIMBA. We note that some BLAST sources are actually

composed of more than one core, as revealed by the higher angular resolution of the

SIMBA map. In addition, the fact that some of the SIMBA cores are associated with

the brightest IRS sources (De Luca et al. 2007) implies that individual BLAST cores

can be linked to the formation of young stellar clusters rather than individual stars.

We have analyzed in detail the positional oÆset between the SIMBA and BLAST

sources to check the consistency of the association criteria. We find that these oÆsets

are randomly distributed with an average value approximately within the BLAST

and SIMBA pointing errors. Therefore, we consider that our associated sources are

in fact the same objects observed by BLAST and SIMBA and the relative oÆsets can

be explained by instrumental pointing and source extraction algorithm.

61

4.2.2 IRAS, AKARI, and MSX

The IRAS bands nicely complement the BLAST maps, especially the 100 µm wave-

band which samples the peak of the SED at temperatures ª 30K. However, the coarse

angular resolution of the IRAS map in most cases prevents assigning reliable fluxes

to the BLAST cores emission. Nevertheless, IRAS data can provide upper limits to

constrain the SEDs, which is useful for BLAST sources located outside the MIPS

spatial coverage. For the cross-correlation between the IRAS PSC and the BLAST

catalog we conservatively set the positional error ≤iras

equal to the semi-major axis of

the IRAS error ellipse (ª 1500 to 8000 for PSC sources in Vela-D). We find associations

for only 26 of the 141 BLAST sources in Vela-D.

In order to characterize the emission of the BLAST cores at 60, 90, 140, and

160µm, we perform a cross-correlation between the BLAST and AKARI catalogs,

for which we consider a positional error ≤akari

= 800 (the pixel size of the images used

to create the source catalog). We find 44 BLAST cores associated with at least one

AKARI source (4 BLAST cores seem to be resolved into two components). However,

the AKARI flux densities have not been used in estimating the best-fit SED (see

section 4.4.2), as we have noted that they are not often consistent with the SED as

determined using all other wavebands. Since we do not have access to the AKARI

maps we have not been able to compare the emission at diÆerent wavelengths, and thus

we have conservatively decided to use the AKARI flux densities only as a reference.

As for the correlation between the BLAST catalog and the MSX PSC, we have set

62

the positional error ≤msx

= 300. We find MSX counterparts for 21 of the 141 BLAST

cores in Vela-D. For comparison with the more sensitive Spitzer data, we find only 15

MSX counterparts in the area cover by MIPS at 24 µm, while we find 55 BLAST cores

associated with 24µm sources (see below). This confirms the importance of having

MIPS data to identify the MIR sources associated with dense cores.

4.2.3 MIPS

In order to search for possible 24 and 70 µm counterparts to the BLAST sources, we

assumed a MIPS pointing error of ≤mips

= 200 and initially considered all the sources

falling within the search radius being associated with a given BLAST core. We found

that 55 and 32 BLAST sources have at least one counterpart at 24 and 70 µm respec-

tively. However, our sample could be aÆected by extragalactic contamination which

is expected to be significant at the 24 and 70 µm completeness levels (as determined

from a comparison of the counts per deg2 between the present survey and the Spitzer

Wide-area Infrared Extragalactic Survey—SWIRE—legacy program). Enoch et al.

(2008), in a similar work, considered a threshold of 3mJy at 24 µm to eliminate most

of the extragalactic interlopers. We find that only 2 BLAST cores have a 24 µm

counterpart below this threshold and thus we assume that our analysis is not being

aÆected by extragalactic contamination. In addition, given the large number of MIPS

24µm sources in the field, we also explore the possibility of finding a chance associ-

ation within the search radius. By calculating the mean number of MIPS sources in

63

an area equivalent to that defined by the search criteria, we estimate the probability

of chance associations to be < 20%. However, if we consider only sources with flux

densities > 3mJy the probability drops to < 10%, and therefore we can conclude that

the 24µm counterparts of the BLAST cores are unlikely to be the result of chance

associations.

4.2.4 IRAC

We perform a cross-correlation between the IRAC and BLAST catalogs also assuming

a pointing error ≤irac

= 200. Now, the probability of finding chance association between

the two catalogs is significantly higher than the estimated value for the 24 µm catalog.

We found that, with a probability of chance associations > 100%, all the BLAST

cores within the area covered by IRAC seem to have at least one IRAC counterpart,

and many of them would be associated with NIR clusters. While we have seen that

Vela-D presents a high e±ciency clustered star formation mode, the 24 µm data sug-

gest that the majority of those cannot be real associations. A study of the potential

association of NIR sources with dust cores is beyond the scopes of this work. Though

NIR photometry has little eÆect on our SED fitting model, it can provide valuable

information about the core evolutionary state. Therefore, we have carried out a pre-

liminary search of IRAC counterparts using the following criteria: (i) spectral index

Æ = d log(∏F∏)/d log(∏), estimated using 2MASS, IRAC, and MIPS 24 µm data, with

values Æ > 0.3; (ii) the source must be detected in the IRAC 8 µm band; and (iii)

64

when no MIPS 24µm flux is available, the flux extrapolated from 2MASS and IRAC

data must be consistent with an upper limit in the MIPS 24 µm band. With these

criteria, we find that 48 BLAST sources have at least an IRAC counterpart.

4.3 Source Photometry

In this section, we describe the specific flux extraction methods finally used to con-

struct the SED of the BLAST cores. The supplementary photometry performed at

1200, 100, 70, and 60 µm using the SIMBA, IRAS, and MIPS maps is discussed in

detail.

4.3.1 SIMBA

In order to create the SED of the BLAST cores, we adopt the flux densities of the

associated SIMBA cores listed in Massi et al. (2007). While CLUMPFIND is expected

to provide consistent flux densities for the resolved MMS cores, flux densities and sizes

of the umms cores are unreliable. In fact, we realized that only the MMS fluxes can

be consistent with the SEDs as defined by the BLAST fluxes. Therefore, we preferred

to estimate the flux densities of the umms cores using aperture photometry. We chose

a circular aperture of radius Rapr

= 1.3 £ 3500 (3500 being the BLAST FWHM at 250

micron), centered over the SIMBA coordinates. Baseline pixel values were estimated

as the median in an annulus between the outer-edge of the main aperture and a second

circle with a radius Rsky

=p

2 Rapr

in order for the annulus to have the same area as

the aperture. If more than a SIMBA MMS source is associated with a single BLAST

65

object, their fluxes are co-added and considered as an upper limit in the SED fit.

However, when a multiple source is composed of at least one umms core, we integrate

the map in an area equivalent to the size of the BLAST source. For that purpose, we

perform aperture photometry using a dynamical aperture of radius Rdyn

= 1.3£Dsource

centered over the BLAST coordinates and an annulus region of the same area. Here

Dsource

is the BLAST deconvolved FWHM, convolved with the SIMBA beam (thus,

an estimate of the source size as seen by SIMBA). For those BLAST sources falling

inside the area mapped by SIMBA but without a SIMBA counterpart, we estimate an

upper limit to the 1.2mm flux density performing aperture photometry with the same

aperture and annulus. However, when the resulting upper limit is less than 0.4 Jy

we retain this value, since it corresponds to the average noise of ª 20mJy/beam

integrated over an aperture of size equal to the BLAST beam.

4.3.2 IRAS

IRAS PSC fluxes are used to complement the BLAST photometry at 100 and 60 µm.

For those BLAST sources without an IRAS counterpart we estimate upper limits

performing direct aperture photometry in the IRAS maps, centered over the BLAST

coordinates. We integrate the 100 µm emission over a circular aperture of radius 14400

and subtract a background estimated as the median in an annulus region of the same

area. For the 60µm band we take into consideration the strong elongation of the

PSF along the scan direction. Therefore, we use an elliptical aperture aligned with

66

the scan direction, with semi-axes of 10800£ 7200 and an elliptical annulus region of

the same area. The size of the apertures was chosen so that aperture measurements

approximately reproduce the flux densities from the IRAS PSC. We made this com-

parison using the sample of BLAST sources with an IRAS counterpart and found no

significant bias between both flux extraction methods, with scatters of 23% at 60 µm

and 26% at 100µm.

4.3.3 MIPS

After experimenting diÆerent photometric techniques (see discussion in section 4.1.1),

we found that catalog fluxes (i.e., PSF fluxes in this case) are more appropriate in

most cases. Therefore, we take the flux densities of the MIPS sources associated with

BLAST cores, as determined from the cross-correlation between the BLAST and MIPS

catalogs. If more than a MIPS source is associated with a single BLAST object, their

fluxes are co-added and the total flux is considered as an upper limit in the SED fit.

However, for various 70µm sources with a “core +halo” structure the flux densities

are clearly underestimated. In these cases, we performed aperture photometry and

ignored the flux densities from PSF photometry. We used and aperture of radius

Rapr

= 8.75 pixel (with 1 pixel = 400 in the MIPS 70µm image) and an annulus region

delimited by two circles of radii Rsky1

= 9.75 pixel and Rsky2

= 16.25 pixel. Following

the MIPS manual2 we also applied a correction factor c = 1.308. For those BLAST

2http://ssc.spitzer.caltech.edu/mips/apercorr/

67

sources falling inside the area mapped by MIPS but without a 70 µm counterpart,

we estimate upper limits using dynamical aperture photometry. As for the SIMBA

map, we used a dynamical aperture of radius Rdyn

= 1.3 £Dsource

centered over the

BLAST coordinates and an annulus region of the same area, where now Dsource

is the

BLAST deconvolved FWHM, convolved with the MIPS 70 µm beam. Flux densities

at wavelengths ∑ 24µm do not aÆect significantly the fit of the SED (see section 4.4.2)

and therefore we do not need to estimate additional upper limits.

4.4 Spectral Energy Distribution of the Dense Cores

Dense cores present a complex structure which may depend on the environmental

conditions and their evolutionary stage. In order to model the observed emission,

the equation of radiative transfer (equation 2.2) should be solved for a given set of

dust properties and temperature and density distributions. A number of radiative

transport codes have been developed by diÆerent authors to model starless cores (e.g.

Evans et al. 2001; Shirley et al. 2005) and accreting protostars (e.g. Whitney et al.

2004) in the case of isolated star formation. In this section we describe how we model

the observed SEDs in order to estimate the physical parameters of the dense cores.

4.4.1 The SED Model

Here, we follow the approach adopted by Chapin et al. (2008) and Netterfield et al.

(2009) who use a simple, single-temperature SED model to fit the millimeter–MIR

photometry of the BLAST cores (described in section 4.3), which will allow us to infer

68

their main physical parameters. The temperature and density variations are much less

critical in starless than proto-stellar cores, and therefore an isothermal model should

provide an appropriate fit of the SED. Proto-stellar cores, in contrast, consist of a

central IR source embedded within a cooler envelope and thus cannot be described

with a single-temperature model. In such cases, fitting the submillimeter/FIR part

of the SED with a single-temperature model (and leaving MIR data as upper limits)

one can estimate the envelope properties.

In section 2.1.4 we have calculated the observed flux density of a cloud of constant

temperature, density, and dust properties, subtending a solid angle ¢≠. In the case

of a dense core at a given distance, we can rewrite equation 2.3 in terms of the dust

mass, Md, the dust opacity, ∑∫ , and the distance, d:

F∫ =Md∑∫

d2

B∫(T ) . (4.3)

The dependence of the dust opacity with radiation wavelength is conventionally writ-

ten as ª ∏°Ø in the range 30µm ∑ ∏ ∑ 1mm, where Ø is the dust emissivity index.

Then, we can express the model SED of the dense cores as a function of three fitting

parameters: a constant factor, A, the dust temperature, T , and the emissivity index,

Ø. Following the notation adopted in Chapin et al. (2008):

S∫ = A

µ∫

∫0

∂Ø

B∫(T ) , (4.4)

where the constant factor is written in terms of the total core mass, Mc, the dust

69

opacity evaluated at ∫0

, ∑0

, and the distance to the object:

A =Mc∑0

Rgdd2

. (4.5)

Here, we adopt a dust-to-gas mass ratio Rgd = 100, required in the denominator

for Mc to be the total mass of the core rather than just the dust mass. The value

of ∑0

has been estimated in diÆerent environments, from cold dense regions to the

diÆuse ISM, (e.g. Hildebrand 1983; Draine and Li 2007) though it is still uncertain

by an order of magnitude. Netterfield et al. (2009) have obtained ∑0

= 16 cm2 g°1,

evaluated at ∫0

= c/250µm by comparing the BLAST dust emission to the estimated

gas mass from C18O data (Yamaguchi et al. 1999). Here, we use the same value for

internal consistency.

Equation 4.4 contains three free parameters, A, Ø, and T , and therefore, at least

three flux densitites in diÆerent wavebands are needed to perform the fit. However,

there is a strong degeneracy between Ø and T , and there may be a large spread in the

values of these parameters which can provide an equally good fit of the SED, partic-

ularly for the coldest objects non detected at IR wavelengths. Simple dust emission

models predict a value Ø ' 2 at millimeter wavelengths, though it is thought to vary

in the range 1 to 2, from warmer and denser regions to more diÆuse environments.

Dupac et al. (2002) attempted to fit both parameters and suggested that there is an

inverse correlation between T and Ø. However, it has been shown recently (Shetty

et al. 2009a) that this apparent correlation naturally arises from least square fits due

70

to the typical uncertainties in flux density measurements. Therefore, the observed T–

Ø correlation may not be a physical property of dust in the ISM. We thus choose to fix

a value Ø = 2 to reduce the inferred errors in temperature, though possible variations

in Ø must be considered for a correct interpretation of the estimated temperatures.

4.4.2 The Fitting Procedure

An isothermal modified blackbody model (equation 4.4) is fitted to all of the available

photometry, from millimeter to MIR wavelengths, using ¬2 optimization. Then, core

temperatures are obtained directly from the fit, and core masses are calculated from

the fitted constant factor and the assumed dust properties (equation 4.5). The FIR

luminosity of the cores is estimated by integrating the SED model (see section 2.1.4)

over a wide spectral range (from 1µm to 5mm).

The SED fitting procedure has been explained in detail in Chapin et al. (2008)

and Truch et al. (2008). The ¬2 function can be written in the simplest form as:

¬2 =X

i

(si ° si)2

æ2

i

, (4.6)

where si are the flux density measurements at wavelength ∏i, æi their associated

errors, and si are the model predictions. BLAST measurements are band averaged flux

densities and therefore, they must be compared with the band averaged flux densities

of the SED model:

71

si =

ZTi(∫) S∫ d∫ , (4.7)

where, the SED model, S∫ , is defined in equation 4.4, and Ti(∫) is the normalized

filter transmission profile for each BLAST band (see Fig. 2.4). Flux density errors,

æi, must include statistical as well as calibration uncertainties. BLAST calibration

uncertainties are highly correlated, since the three bands were calibrated using just

one object (Truch et al. 2009). Therefore, equation 4.6 should be written in terms of

the data covariance matrix, C, in order to account for correlated errors. In matrix

notation:

¬2 = (s° s) C°1 (s° s)T , (4.8)

where now s and s are the vector of measured fluxes and model predictions re-

spectively, and “T” denotes the transposed matrix. The diagonal elements of C cor-

respond to the variances, æ2

i , of the flux density measurements. For the BLAST data,

æ2

i , are calculated as the quadrature sum of the statistical (photometric) and calibra-

tion uncertainties (10%, 12%, and 13% at 250, 350, and 500 µm respectively). The

oÆ-diagonal terms are estimated from the Pearson correlation coe±cients, assuming

completely correlated uncertainties (see Truch et al. 2009, table 1). Errors associated

with flux measurements from other instruments are considered uncorrelated.

In section 4.3 we have discussed how we estimate upper-limits for BLAST cores

without any counterpart at a given wavelength, to help constraining their SEDs. In

72

addition, we noted that BLAST and MIR flux densities cannot be fitted simultane-

ously with a single-temperature model, as they originate from volumes of material at

diÆerent temperatures. Therefore, in most cases flux density measurements at MIR

wavelengths (∑ 24µm) must be considered as upper-limits. In order to include them

in the calculation of ¬2 we use the “survival analysis” (see Chapin et al. 2008). Given

un upper-limit, the likelihood of the model flux density is calculated and the nega-

tive log-likelihood function is added to the right side in equation 4.8, giving the final

expression that we minimize to perform the fit.

As a general rule, we have used all photometry at wavelengths ∑ 24µm as upper-

limits. In addition, when more than one catalog source is associated with a BLAST

core, the total flux is also considered an upper-limit (see section 4.3). IRAS PSC

flux densities are estimated over an eÆective area which may encompass the emis-

sion of nearby sources and/or any plateau structure (a circular area of ª 50 diameter

at 100µm), as revealed by maps at other wavelengths. Therefore, IRAS photome-

try measurements are always used as upper-limits. A critical point is to determine

whether 70µm flux densities are consistent with the isothermal SED model or should

be considered also as upper-limits. MIPS 70 µm data lie on the Wien’s side of the

distribution, between the MIR emission from the warmer, inner parts, and the sub-

millimeter emission radiated from a cooler envelop, and therefore, they can strongly

constrain the SED. A careful analysis of preliminary SEDs and maps is needed to

address this point.

73

Uncertainties for the fitting parameters, A and T , and other derived quantities such

as core masses and FIR integrated fluxes, are estimated using Monte Carlo simula-

tions. Simulated data sets are generated from realizations of Gaussian noise, including

correlated and uncorrelated errors, and the ¬2 minimization process is repeated for

each of them. Then, each parameter is characterized by a distribution of values orig-

inated from the simulations, which is used to calculate the 68 % Bayesian confidence

interval.

Finally, once the SED has been fitted, BLAST band-averaged flux measurements

are corrected to quote eÆective flux densities at precisely 250, 350, and 500 µm, allow-

ing a direct comparison with the SEDs. Color-corrected flux densities are calculated

using the expression:

S∫ =SB

SB

S∫ , (4.9)

where S∫ is the SED model flux density evaluated at 250, 350, and 500 µm, SB

is the band-averaged BLAST measurement, and SB is the SED model band-averaged

flux density.

4.5 Bonnor-Ebert Masses

Once the physical parameters of the BLAST cores have been estimated, it is interesting

to investigate their dynamical state. In this section we introduce a specific criterion

for core stability, derived from a careful modelling of starless cores.

74

Observations of intensity profiles of nearby starless cores (see Di Francesco et al.

2007, for a review) reveal that they are far from homogeneous. Detailed modelling of

these profiles have shown that the density structure of starless cores may be correctly

described by Bonnor-Ebert (BE) spheres, i.e., non-singular solutions of the equations

of hydrostatic equilibrium which can be critically stable in the presence of external

pressure.

BE spheres are characterized by radial density profiles with a central “plateau”

and a power-law decrease at large radii, being gravitationally unstable when the mean

density is ª 14 times the density at the BE boundary radius. This stability criterion

can be written in terms of a critical mass, known as the Bonnor-Ebert mass. Following,

for example, Stahler and Palla (2004):

MBE

= 1.0 MØ

µT

10 K

∂3/2

µhni

104 cm°3

∂°1/2

, (4.10)

Where T is the core temperature and hni is the particle density at the edge of the core

(where the internal pressure is equal to the external pressure). However, given the

BLAST resolution and the Vela-D cloud distance, we cannot define the boundaries

of the cores. We, thus, approximate hni as the mean particle density which can be

estimated from the total core mass, Mc, and the linear deconvolved radius, Rdec

:

hni =Mc

4

3

º R3

dec

µmH

. (4.11)

Here we define Rdec

= Ddec

/2, where the linear deconvolved diameter is calculated in

terms of the Vela-D cloud distance, d, and the intrinsic angular size, µdec

(defined in

75

equation 3.1): Ddec

= d µdec

. As we defined in equation 1.1, mH is the mass of the

hydrogen atom, and here we use a mean molecular weight per particle µ = 2.33.

The size of the cores estimated from Gaussian fitting (see section 3.2.2) and the

core temperatures and masses (see section 4.4) are used in this work to evaluate

equation 4.10 and infer the dynamical state of the cores.

76

Chapter 5

RESULTS

5.1 SEDs and Morphology of the Cores

The SED of the 141 BLAST cores found in Vela-D have been characterized and fitted

across the available spectral range, from millimeter to MIR wavelengths. In addition,

we have constructed multi-wavelength thumbnails for each source, providing an ap-

propriate visual reference in terms of the millimeter-MIR catalogs and images used in

this work.

Fig. 5.1 and Fig. 5.2 show two representative examples of SEDs and thumbnails

of the dust cores. The first one corresponds to one of the brightest sources in Vela-D,

BLAST J084848-433225. The black line represents the best-fit modified blackbody

model, where SIMBA, BLAST, and MIPS 70 µm flux densities are fitted while IRAS

and MIPS 24µm data are left as upper limits (see section 4.4.2). From the best

fit, we obtain a temperature of 20.7K. The grey lines indicate the 68% confidence

interval of fitting models, from which we estimate the uncertainties in the fitting

parameters (constant factor and temperature; see section 4.4.1). In this case, we

obtain a total mass of 16 MØ (Mtot

= Rgd

£Mdust

, where we adopt a dust-to-gas mass

ratio Rgd

= 100) and a FIR luminosity of 142 LØ. From the thumbnails, we identify

77

Figure 5.1: Left. SED of one of the brightest sources in Vela-D (see text). Red andblue colors are used to show the BLAST color-corrected flux densities and the ancillarydata, respectively. The yellow asterisks represent the AKARI flux densities, thoughthey are not used in the fit (see section 4.2.2). Right. Multi-wavelength thumbnailsshow the source emission at 1200, 500, 350, 250, 100, 70, 60, 24, and 8 µm fromSIMBA, BLAST, IRAS, MIPS, and IRAC instruments. Each map covers an area of20000 £ 20000 centered on the BLAST source, which is represented by a white circle ofdiameter equal to its FWHM. The position of catalog sources at other wavelengths isshown using crosses or open squares, depending on whether they fall within the searchradius or not, respectively (see section 4.2). IRAC catalog sources are shown usingwhite dots for clarity.

Figure 5.2: Same as Fig. 5.1 for one of the coldest sources in Vela-D (see text).

78

an isolated core well detected at all wavelengths. We note that the combination of

source intrinsic structure and instrumental responses results in a set of clearly diÆerent

images from 1.2mm to 8µm.

The second example (see Fig. 5.2) shows one of the coldest objects in the sample,

BLAST J084542-432721, with an estimated temperature of 11.7K, and mass and FIR

luminosity of 5.7 MØ and 1.5 LØ respectively. Here, we clearly see that the BLAST

measurements reveal a turnover in the SED, contrary to the previous example of a

warmer source where the BLAST bands sample the Rayleigh-Jeans tail of the SED.

As seen in the thumbnails, the BLAST core is not detected at shorter wavelengths,

though it is located near a warmer source revealed by the MIPS 24 and 70 µm bands.

The IRAC images in this and the previous figure illustrate the large number of sources

detected at wavelengths ∑ 8µm. At these wavelengths even pre-main sequence field

stars can be identified, making it very di±cult the search for counterparts of the

BLAST cores (see section 4.2.4).

5.2 Separating Starless and Proto-Stellar Cores

One of the goals of this work is to try to identify an evolutionary sequence from star-

less to proto-stellar cores in terms of the estimated physical parameters. In section 1.4

we have mentioned how we can separate dense cores between these two categories by

observational criteria. Here we define as proto-stellar cores those BLAST sources asso-

ciated with a MIPS 24µm counterpart(s) (see section 4.2.3), which provides evidence

79

of the presence of an embedded protostar. In addition, we use the association with

MSX point sources for those BLAST cores falling outside the MIPS 24 µm coverage.

Following this criterion we find a fraction of starless to proto-stellar cores Ns

/Np

= 1.3

in Vela-D. However, we note that if we restrict the starless/proto-stellar comparison to

the area covered by MIPS at 24 µm, we obtain a significantly lower ratio Ns

/Np

= 0.6.

This may be a consequence of lacking sensitive Spitzer observations in part of the

Vela-D region considered here (see section 4.2.2 for a comparison between the number

of MIR counterparts found by MIPS and MSX ), but also because the area mapped

at 24µm is in a later evolutionary state.

In order to check the robustness of this method, we have used our preliminary

search for IRAC counterparts (see section 4.2.4) as well as other signpost of star

formation such as bipolar jets and molecular outflows available from the literature

(Giannini et al. 2005, 2007; De Luca et al. 2007). We find that ª 69% of the BLAST

sources classified as starless cores within the IRAC coverage do not have an IRAC

candidate counterpart. As for the cores classified as proto-stellar within the IRAC

coverage, ª 73% of them do have an IRAC candidate counterpart(s). Therefore, our

quick search for NIR associations show that MIPS 24 µm data provide an appropriate

statistical characterization of these two stages of evolution, though a more careful

analysis should be done to extend the SED of the dense cores to NIR wavelengths (see

De Luca et al. 2007, for example). In addition, we have found that 13 BLAST cores,

classified as proto-stellar (except BLAST J084822-433152), all are associated with H2

80

jets. The core BLAST J084822-433152 represents an example of the need of NIR

observations to ensure a correct classification. Finally, we have identified the BLAST

cores associated with molecular outflows in Vela-D (Elia et al. 2007), all of which have

been correctly classified as proto-stellar cores except BLAST J084805-435415 (also

having an IRAC counterpart).

In Fig. 5.3 we show a few examples of BLAST cores in Vela-D. Panel (a) shows

that the core BLAST J084805-435415 is clearly detected by IRAS at 60 and 100µm,

and presents a strong emission at 24 and 70 µm, though it is not identified in the

MIPS catalogs. Panel (b) shows another example of a core detected by IRAS in which

the presence of MIPS compact emission but no MIPS counterparts makes somewhat

uncertain the starless/proto-stellar classification. Furthermore, we find 26 BLAST

cores detected in the MIPS 24 µm band but not detected in the 70µm band (see

panel(c) in Fig. 5.3). In ∏ 30% of these cases, the MIPS 70 µm map shows a compact,

weak emission associated with the BLAST core, as seen in Fig. 5.3, panel (d). It

seems that a combination of diÆerent instrumental sensitivity in the MIPS bands

and geometrical projection eÆects (relevant for sources with an embedded protostar;

see, e.g., Whitney et al. 2004; Robitaille et al. 2007) may be masking the 70 µm

counterpart. However, we have seen (section 4.2.3) that the probability of chance

associations between the BLAST and MIPS 24 µm catalogs is ª 10%, and therefore

we cannot discard a possible false association, especially in some of the cases with

no significant 70µm emission. In addition, we found two BLAST cores associated

81

with 24µm sources with fluxes below the threshold selected to eliminate most of the

extragalactic objects (see section 4.2.3).

82

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83

5.3 Distribution of Physical Parameters

Once we have separated starless and proto-stellar cores, we are interested in the anal-

ysis of their physical parameters. As the dense cores evolve towards higher central

condensations, the accretion process results in an increase in luminosity, followed by a

gradual decrease of the core mass (material from the envelope is transferred to a central

compact object and part is also ejected into the ISM). In addition, we have seen that

most of the luminosity is absorbed and re-processed by dust, leading to an increase

of the dust temperature. Therefore, we could expect that the luminosity-to-mass ra-

tio, L/M , and the temperature of the cores will increase with time, characterizing

and evolutionary sequence. Fig. 5.4 shows the distribution of temperatures, masses,

luminosities, and luminosity-to-mass ratios of the BLAST cores in Vela-D.

5.3.1 Temperature, Mass, and Luminosity of the BLAST Cores

Before analyzing the distribution of physical parameters, we should understand the

typical uncertainties on the values estimated from the SED fits. Statistical errors

depend on the flux density uncertainties and the number of data points used in the

fit, and are calculated using Monte Carlo simulations (see section 4.4.2). We have

obtained the following mean errors for the physical parameters of the dense cores:

ª 10% for temperature, ª 40% for mass, ª 30% for luminosity, and ª 50% for the

luminosity-to-mass ratio. In addition, there might be significant uncertainties related

to the values of distance, opacity, emissivity index, and dust-to-gas ratio used in the

84

SED fitting model (see section 4.4.1). For example, if we use Ø = 1.5 (instead of Ø = 2)

the estimated temperatures would increase by º 10%. On the other hand, using

∑0

= 10 cm2 g°1 (from Hildebrand 1983, instead of ∑0

= 16 cm2 g°1) the estimated

masses would result ª 60% higher. In addition, a similar mass increase would result

if we consider a distance d = 900 pc (with 700± 200 pc being the estimated distance

to Vela-D). However, if we assume that these parameters are nearly constant for the

population of dense cores in the Vela-D cloud, we still can compare the physical

properties of starless and proto-stellar cores, taking into consideration only statistical

errors.

The temperature distribution (Fig. 5.4, top-left panel) is characterized by a wide

peak at T ª 15K, with sharp cutoÆs at the low (11–12K) and high (18–20K) end

of the distribution. The median temperature for the whole sample is 15.4K, higher

than, for example, in the Pipe cores (Rathborne et al. 2008) and in the Vela-C cores,

suggesting that cores in Vela-D are in a later evolutionary stage. However, we find

little diÆerence between starless and proto-stellar cores, with median temperatures of

14.6K and 15.7K respectively.

The overall mass distribution is presented in the top-right panel of Fig. 5.4, showing

a clear decrease from lower to higher masses. With a median value for the whole sample

of 4.7 MØ, very few cores are found with masses > 10 MØ. The range of observed

values is consistent with the formation of low and intermediate-mass stars. There is

no significant diÆerence between the median masses of starless and proto-stellar cores,

85

Figure 5.4: Temperature (top-left panel), mass (top-right panel), (FIR) luminosity(bottom-left panel), and luminosity-to-mass ratio (bottom-right panel) distributionsof the BLAST cores in Vela-D (grey histograms). The distributions of starless andproto-stellar cores are shown using red dashed lines and yellow solid lines respectively.

86

with values of 4.6 MØ and 4.8 MØ respectively.

As for the FIR luminosities (Fig. 5.4, bottom-left panel), the distribution shows a

strong peak at ª 4 LØ, with a median luminosity of 4.6 LØ (11 cores with luminositites

> 40 LØ are not shown in the histogram). Starless and proto-stellar cores seem to have

diÆerent median luminosities, 4.4 LØ and 7.5 LØ respectively.

The luminosity-to-mass ratio of the cores is essentially equivalent to the tem-

perature and depends, therefore, only on the shape of the SED (being a distance-

independent quantity). In Fig. 5.4, bottom-right panel, we see that starless and

proto-stellar cores show a somewhat diÆerent L/M distribution, having median val-

ues of 1.0 LØM°1

Ø and 1.6 LØM°1

Ø respectively.

5.3.2 Color–Color, Luminosity–Mass, and Spatial Distribution Plots

In order to further characterize the core population in Vela-D, we have investigated

the distribution and potential correlation between diÆerent parameters by means of

2-dimensional plots. In this section we present the color–color, luminosity–mass, and

spatial distribution plots for the BLAST cores in Vela-D (see Fig. 5.5, 5.6, and 5.7).

Fig. 5.5 shows a color–color plot where we represent the flux density ratio F250

/F350

as a function of F250

/F500

, which are quantities depending on the core temperature.

We see a large concentration of BLAST sources located near the line of constant

emissivity index Ø = 2, being consistent with the adopted value in our SED-fitting

model (ª 44% and 56% of the cores are located above and below the line Ø = 2).

87

Figure 5.5: Color–color plot for the BLAST cores in Vela-D (see text). The ratiosF

250

/F350

and F250

/F500

have been calculated from the color-corrected flux densities(see section 4.4.2). Modified blackbody models with Ø = 0, 1, 2, 3, and temperaturesranging from 3 to 100K are overplotted as dashed lines. Starless and pre-stellar coresare represented by open circles and crosses respectively. The error bars account forstatistical as well as calibration uncertainties and are shaded for clarity.

88

Starless and proto-stellar cores show a similar distribution in the color–color plot,

though the latter tend to be located at lower Ø values: the ratio of the number of

sources consistent with Ø < 2 to Ø > 2 is ª 1.5 for proto-stellar cores, compared to a

ratio of ª 1.1 for starless cores.

A plot of the luminosity of the cores as a function of mass, the luminosity–mass

plot or simply L°M plot, is shown in Fig. 5.6. The plot shows a high concentration of

sources at temperatures . 15K, or equivalently . 0.65 LØ M°1

Ø , consistent with the

temperature and luminosity-to-mass ratio distributions shown in Fig. 5.4 (bottom-

right panel). If the BLAST cores have actually diÆerent temperatures they would

move along their constant flux density locus (evaluated at 250 µm in the figure), down

and right if they are colder than the estimated temperatures. We note that a small

group of nine bright objects (F250

> 100 Jy) appear clearly separated from the main

concentration of sources, which have typical flux densities . 30 Jy at 250µm. They

are mostly proto-stellar cores, detected by AKARI and MIPS (at 24 and 70µm), and

five of them correspond to known IRS sources (Liseau et al. 1992) associated with

small NIR clusters.

In Fig. 5.7 we present the spatial distribution of dense cores in Vela-D (also shown

in Fig. 3.4) with additional information of core temperatures and masses, as well as the

starless/proto-stellar core classification. This figure shows some interesting features.

For example, we see that the BLAST cores associated with IRS sources (shown in

Fig. 3.6) and thus, classified as proto-stellar cores, are among the warmer and more

89

Figure 5.6: FIR luminosity versus mass for the BLAST cores in Vela-D. Error barsshow the uncertainties in luminosity and mass estimated from the range of SED modelsconsistent with the available photometry (see section 4.4.2). Errors involving theadopted values of distance, Ø, ∑

0

, and gas-to-dust mass ratio are not included. Thedashed lines are loci at constant T = 10 to 30 K assuming a modified blackbody modelwith Ø = 2. Also shown are the loci at constant 250 µm flux density, evaluated from3 to 1000 Jy, using the same model. The “+”, “¶”, and “§” signs mark the BLASTsources without AKARI, MIPS 24µm, or MIPS 70 µm counterparts respectively.

90

Figure 5.7: Spatial distribution of cores in Vela-D. Starless and proto-stellar cores arerepresented by open circles and crosses respectively, overlaid with a grey-scale imageof the BLAST 250 µm map (using the same saturation levels as Fig. 3.4). Increasingcore temperatures (arranged in four intervals delimited by T = 13, 15, and 17K) areshown using red, yellow, green, and blue colors. Core masses are indicated by thesize of the signs, circles and crosses, separated in three mass intervals: M < 2 MØ,2 < M < 10 MØ, and M > 10 MØ.

91

Starless Proto-stellar

All M > 4.2 MØ M > 11 MØ All M > 4.2 MØ M > 11 MØ

T [K] 14.6 13.1 12.9 15.7 15.5 17.2

Mcore

[MØ] 4.6 7.0 15.3 4.8 8.7 16.5

Lfir

[LØ] 4.4 4.5 7.0 7.5 10.9 84.7

Lfir

/Mc

[LØM°1

Ø ] 1.0 0.5 0.5 1.6 1.5 2.7

Table 5.1: Temperature, mass, luminosity, and luminosity-to-mass ratio median valuesof starless and proto-stellar cores in Vela-D, evaluated for the whole sample (All), andthe fraction of cores above the completeness limits M > 4.2 MØ and M > 11 MØ forsources with T > 12K and T > 10K respectively.

massive cores in Vela-D (these are some of the brightest sources shown in Fig. 5.6).

We see that the more massive cores are rarely found in isolation, often being part

of small groups of sources and associated with strong dust continuum emission. In

contrast, BLAST sources found in isolation tend to be low-mass cores. We also note

an interesting group of sources found along the filament at coordinates l ª 262.88±,

b ª 0.27±, that is composed of mainly cold, massive starless cores.

5.4 Do Starless and Proto-Stellar Cores Have DiÆerent Properties?

As we have seen, it is not completely clear that starless and proto-stellar cores can be

separated according to their observed physical parameters. They are placed in similar

regions in the L°M and color–color plots, and the histograms do not show a sharp

separation between the two types of core, though the proto-stellar cores appear to be

somewhat warmer than the starless cores.

92

However, if we restrict our analysis to mass ranges for which our source-extraction

technique is more complete (Netterfield et al. 2009), we find that the temperature

diÆerence between starless and proto-stellar cores becomes more significant. For ex-

ample, sources with masses > 11 MØ (the completeness level for T > 10K) have

median temperatures of 12.9 and 17.2K for starless and proto-stellar cores respec-

tively (see table 5.1). In addition, the diÆerences in median luminosities and L/M

ratios between starless and proto-stellar cores, which were mentioned earlier, become

larger if we consider only those cores above this completeness level. Thus, starless

cores with masses > 11 MØ have medians of 7.0 LØ and 0.5 LØM°1

Ø respectively, sig-

nificantly lower than the median luminosity and median L/M ratio of 84.7 LØ and

2.7 LØM°1

Ø found for proto-stellar cores. Remarkably, the diÆerence between median

masses of starless and pro-stellar cores in Vela-D is not significant even for the sample

of cores above the completeness level.

These results suggest that starless and proto-stellar cores are characterized by

diÆerent values of temperature and luminosity-to-mass ratio. However, median values

may be aÆected by the low-number statistics at the high mass end of the distribution,

as we find only 29 BLAST cores with masses > 11 MØ. Therefore, we should be

careful interpreting the observed diÆerences between starless and proto-starless cores.

While we do observe an increase in median temperature and L/M ratio from starless

to proto-stellar cores and thus, consistent with an evolutionary sequence, we also

note that there should be a smooth transition between these phases. This is in fact

93

suggested by the partial overlap between the distribution of physical parameters of

starless and proto-stellar cores.

Besides the uncertainties introduced by completeness and low-number statistics,

the diÆerent instrumental resolution and sensitivities may aÆect the distinction be-

tween starless and proto-stellar cores. An example of proto-stellar core with an unex-

pected low temperature is shown in Fig. 5.3, panel (e). It is clearly detected by MIPS

at 24 and 70µm, but its temperature from the best-fit SED is only 13.14K. Such a cold

source is not expected to emit a significant flux density at wavelengths ∑ 70µm and

therefore we can considerer two possible scenarios: (i) either the 70µm flux density

is emitted by a warmer central core and therefore the emission detected by BLAST

comes from a colder envelope; or (ii) there are two separate nearby sources, a cold,

starless core detected only by BLAST, and a warmer proto-stellar source detected by

MIPS. With the current available data we are not able to discriminate between these

two possible scenarios and, while recognizing some ambiguity in this and similar cases,

we opt to be consistent with our association criteria and classify as proto-stellar those

cores with 24µm counterparts.

We also find starless cores significantly warmer than the typical temperatures we

could expect (> 15K; see, e.g., Di Francesco et al. 2007). This might be a consequence

of lacking enough sensitivity at MIR wavelengths to detect the most embedded pro-

tostars. On the other hand, it has been shown (e.g., Shirley et al. 2005) that starless

cores may be colder on the inside and warmer on the outside, because they are heated

94

externally by the interstellar radiation field and they lack an internal source of heat-

ing. In fact, we have seen that the temperature of the most massive starless cores

(M > 11 MØ) seem to be lower than that of the low-mass starless cores (see table 5.1

and Fig. 5.7), being consistent with the dust temperature depending on the degree of

shielding from an external radiation field (as shown by radiative transfer calculations).

Therefore, the higher temperature of some starless cores (especially the less-shielded

low-mass and/or isolated cores) could be the consequence of a warmer surrounding

medium1 heated by the interstellar radiation field.

The eÆect of temperature variations along the line of sight has been analyzed

elsewhere (Shetty et al. 2009b) and could have implications in our results obtained

from single-temperature models. Furthermore, the use of a constant emissivity index

(Ø = 2) in our SED fitting model may contribute to mask possible temperature

variations between starless and proto-stellar cores. We note that proto-stellar cores,

since they are warmer and denser than starless cores , could be better characterized

by an emissivity index Ø < 2, thus resulting in higher model fitting temperatures.

5.5 Mass Spectrum

We have mentioned in section 1.4 that the mass distribution of pre-stellar cores, i.e.,

the core mass function (CMF), is one of the key concepts which can be used to

discriminate between diÆerent star formation models. Furthermore, recent studies

1Schlegel et al. (1998) estimated an equilibrium temperature of ª 18 K for low density interstellardust in the Galactic plane.

95

Figure 5.8: Combined starless and proto-stellar CMF of the BLAST cores in Vela-D.Error bars show the Poisson uncertainty for each bin. The best-fit to the CMF (apower law with slope Æ = °2.2) is represented by the solid line. The vertical dot-dashed and dotted lines show the completeness limits for sources warmer than 10 and12K respectively. The turnover seen at the low mass end is likely to be a consequenceof incompleteness.

suggest that the distribution of stellar masses at birth, i.e., the IMF, is determined at

the pre-stellar stage. Thus, a considerable eÆort is currently being done to constrain

the IMF both theoretically and observationally.

We have investigated the combined mass spectrum in Vela-D, including starless2

and proto-stellar cores (see Fig. 5.8). Individual core masses are placed in logarith-

mically spaced bins so that we can fit a standard power law (dN/dM / MÆ) to the

resulting mass function. The error bars are estimated from the Poisson uncertainty

for each bin. Then, fitting the CMF for masses M > 4 MØ we find a slope Æ = °2.2

with a correlation coe±cient r = 0.99. However, we note that the best-fit power law

2We are thus using starless cores that may not be pre-stellar (see section 5.6).

96

is somewhat dependent on the histogram binning and therefore there is no unique

slope Æ for a given mass distribution. In fact, by varying the bin width from ª 1.7 to

4.2MØ, we find values from Æ ' °2.1 to Æ ' °2.5. Therefore, we empirically assign

the slope Æ = °2.3± 0.2 to the CMF for masses M > 4 MØ in Vela-D.

This value is very similar to the slopes found in other star forming regions such as

Orion (Æ = °2.35 in the mass range M > 2.4 MØ; Nutter and Ward-Thompson 2007)

and Perseus, Serpens and Ophiucus (Æ = °2.3± 0.4 in the mass range M > 0.8 MØ;

Enoch et al. 2008). In contrast, we have found a CMF steeper than the previous mass

functions obtained in Vela-D, with slopes of Æ ª °1.4 to °1.9 (Massi et al. 2007) and

Æ ª °1.3 to °2.0 (Elia et al. 2007), which are probably aÆected by the smaller source

samples used to compute the CMF and the inability to detect many of the coldest,

low-mass sources. We note that evaluating the CMF only for the BLAST cores with

SIMBA counterparts we find a slope consistent with the range of values reported by

Massi et al. (2007). On the other hand, Netterfield et al. (2009) have found a steeper

slope of Æ = °2.77± 0.16 in the mass range M > 14 MØ (the completeness limit for

sources warmer than ª 8.5K) for the BLAST cores in the VMR. Thus, it seems that

the slope of the CMF depends on the mass range used to evaluate the mass function as

well as the statistical significance of the sample, besides other eÆects such as variations

of Ø among diÆerent cores or temperature variations inside them. In addition, we note

that the VMR overall slope may also be aÆected by the great variation in physical

conditions and by distance eÆects. Netterfield et al. (2009) have shown that cold

97

cores follow a steeper mass function than warmer cores. In that sense, their finding of

Æ = °2.55± 0.20 for the BLAST cores in Vela-C is consistent with our result, being

the Vela-D cloud in a later evolutionary phase.

The CMFs computed in Vela-D and other star-forming regions have similar shapes

to the Salpeter IMF (Salpeter 1955) for main-sequence stars in the solar neighbour-

hood, characterized by the slope Æ = °2.35. All of these observations suggest that the

shape of the IMF is a direct consequence of the CMF. However, compared to previous

measurements of the CMF, our result is based on a robust determination of the core

temperature.

5.6 Dynamical State of the Cores

Ideally, the CMF should be computed for pre-stellar cores only, rather than the com-

bined population of starless and proto-stellar cores. However, the relatively small

number of sources in the two sub-samples prevents us from evaluating separately the

CMF. In addition, based on our current data, we cannot distinguish the subset of star-

less cores which are gravitationally bound and are therefore truly pre-stellar, which

would require the analysis of the velocity dispersion within the cores using molecular

line observations. However, in section 1.4 we have seen that an indirect separation

between starless and pre-stellar cores could be attempted in terms of the comparison

between the observed core masses and the theoretical masses for cores in critical equi-

librium. We have used the BE mass criterion introduced in section 4.5 to analyze the

98

Figure 5.9: Ratio of the total core mass to the Bonnor-Ebert mass plotted as afunction of the total mass. BE masses are calculated using the core temperature andthe average density, estimated from the deconvolved size and mass of the BLASTcores (see section 4.5). Starless and proto-stellar cores are represented by red andblack symbols respectively.

dynamical state of the cores.

Evaluating equation 4.11 for those cores with deconvolved diameter larger than the

beam (thus, eliminating those cores with unrealistically small sizes), we have obtained

a median particle density of 1.6£104 cm°3, with values ranging from 3.7£103 cm°3 to

1.5£105 cm°3. Then, the BE masses of these cores are estimated using equation 4.10.

Fig. 5.9 shows a plot of the ratio of the total core mass to the BE mass, Mc/MBE

,

as a function of core masses. If we consider that the dense cores are approximately

spherical in shape and assuming that they are supported against gravity only by

99

thermal pressure, then the horizontal line drawn at Mc/MBE

= 1 would separate those

cores in equilibrium from those in non-equilibrium configurations. Thus, dense cores

with Mc/MBE

> 1 could be potentially collapsing to form stars. However, we note that

elongated cores can be more stable than spherical BE-like cores. In addition, we have

seen that non-thermal support due to magnetic and/or turbulent pressures may have

significant eÆects on the evolution of cores. Therefore, the Mc > MBE

criterion should

be regarded as a necessary but not su±cient condition for core collapse. In addition,

we note that the ratio Mc/MBE

critically depends on how Mc/Rdec

is observationally

defined, which is clearly instrumental dependent.

We see in Fig. 5.9 that the Mc/MBE

ratio seems to be proportional to Mc, and we

note that this is a consequence of its operational definition, giving that Mc/MBE

/

(Mc/T Rdec

)3/2. This figure suggests that the low-mass cores observed by BLAST are

more likely to be transient objects rather than gravitationally bound cores, being the

transition from Mc/MBE

< 1 to Mc/MBE

> 1 in the mass range Mc ' 3 to 7 MØ.

We find a median value MBE

= 4.6 MØ or Mc/MBE

= 1.1 for all cores in Vela-D,

which is near the critical value. If we restrict our analysis to the more massive cores,

with masses Mc > 14 MØ, we find a median value significantly higher, Mc/MBE

=

8.6, indicating that high-mass cores are more likely to be gravitationally unstable.

Furthermore, we find very little diÆerence between starless and proto-stellar cores,

with ª 50% and ª 60% of them being consistent with Mc/MBE

> 1, respectively.

The ratio Mc/MBE

< 1 for ª 40% of the proto-stellar cores could be a consequence of

100

the presence of a central YSO having accreted a significant fraction of the core mass

and increased the temperature of the envelope. On the other hand, the fraction of

starless cores with Mc/MBE

> 1 could be classified as pre-stellar. However, given the

uncertainties in the estimated physical parameters and, even more importantly, the

various assumptions required by the BE criterion, the distinction between starless and

pre-stellar is still quite uncertain.

5.7 Summary and Conclusions

In this work, we have presented a detailed analysis of the earliest stages of star for-

mation in the Vela-D region. Using the BLAST maps at 250, 350, and 500 µm and

additional data from previous MIR, FIR, and millimeter continuum observations, we

have investigated the physical nature of the dense core population in the Vela-D

molecular cloud. We summarize our results as follows:

• We have found that ª 30% of the BLAST cores falling within the area covered

by SIMBA (at 1200µm) and MIPS (at 24 and 70 µm) are not detected at any of

these wavelengths, thus demonstrating the importance of observing the dense,

cold cores near the peak of the SED.

• Combining the available photometry from millimeter to MIR wavelengths we

have constrained and fitted the SED of the cores found by BLAST, thus obtain-

ing more accurate estimates of their physical parameters, particularly for the

warmer sources.

101

• Based on the associations of BLAST sources with YSO we have separated the

population of cores between starless and proto-stellar cores, finding a ratio

Ns

/Np

= 1.3 in Vela-D. However, we note that the sensitivity of the available

MIR data critically aÆects the intrinsic starless to proto-stellar core ratio for a

given star-forming region.

• We find a smooth transition from starless to proto-stellar cores in terms of

their physical parameters. Proto-stellar cores have a median temperature and

a median L/M ratio higher than starless cores, suggesting that they are in a

later evolutionary stage. The similarity between the median masses of starless

and proto-stellar cores suggests that the mass is not significantly aÆected by the

transition between the two phases.

• The CMF for all the BLAST cores in Vela-D is characterized by a power law

with slope Æ = °2.3 ± 0.2, consistent with other millimeter surveys and the

Salpeter IMF.

• The median ratio of the observed masses to the BE masses is found to be near

the critical value, Mc/MBE

= 1.1. The Mc/MBE

versus Mc distribution suggests

that the observed low-mass cores tend to be gravitationally unbound.

102

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