mapping the milky way with sdss, pan-starrs and lsst

Mapping the Milky Way with SDSS,Pan-STARRS and LSST

Zeljko Ivezic

Mario Juric, Nick Bond, Alyson Brooks, Robert Lupton, et al.

University of Washington, Princeton University

Institute for Astronomy, Honolulu, Sep 31, 2005

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Outline

1. Motivation: a detailed description of the Milky Way

2. Overview of SDSS and LSST

3. Photometric Parallax Method

4. The properties of thin and thick disks

5. Large overdensity towards l = 300, b = 60 at ∼10-15 kpc

6. The Milky Way Kinematics

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The Milky Way Maps• The top left panel is not really

the Milky Way :) but it shows

the distance range probed by

SDSS-detected main sequence

stars (out to ∼15 kpc)

• SDSS RR Lyrae and other lumi-

nous tracers, and 2MASS M gi-

ants, demonstrate that the Milky

Way halo extends to ∼100 kpc

and has a lot of substructure

• What is the structure of the disk

component out to a few tens of

kpc?

• SDSS has obtained excellent

photometric data for close to 100

million stars. How can one utilize

these data for studying the disk

component?

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Constraining Thin/Thick Disk+Halo Models• Observationally, ρ(z|R = R�) is well fit by a sum of

double exponential (thin and thick disk) and power-

law profiles.

• But, very different models (top: thin and thick disk

without halo; middle: single disk and halo, bottom:

the difference) can produce the same ρ(z|R = R�)

• A large sky area is needed to break model degenera-

cies (pencil beam surveys are not conclusive)

• SDSS is the first survey with the required data

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Overview of SDSS

• Imaging and Spectroscopic Survey

– ∼10,000 deg2 (1/4 of the full sky)

– 5 bands (ugriz: UV-IR), 0.02 mag photometric accuracy

– < 0.1 arcsec astrometric accuracy

– Over 100,000,000, mostly main sequence, stars

– Spectra for >200,000 stars (radial v to ∼10 km/s)

• Advantages for studying the Milky Way structure

– Accurate photometry: photometric distance estimates

– Numerous stars: small random errors for number density

– Large area and faint limit: good volume coverage

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Smolcic et al. (2004)

SDSS Color-color diagrams• Wide wavelength coverage of

SDSS bandpasses, together with

accurate and robust photometry,

encodes a large amount of infor-

mation

• Stars on the main stellar locus

are dominated (∼98%) by main

sequence stars

• The position of main sequence

stars on the locus is controlled by

their spectral type/effective tem-

perature/luminosity, and thus

can be used to estimate distance:

photometric parallax method

• A preview of results from Juric et

al. (2005)

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Large Synoptic Survey Telescope

• LSST = d(SDSS)/dt: an 8.4m telescope with single expo-

sures reaching V∼24.5 over a 9.6 deg2 FOV: the whole (observ-

able) sky in two bands every three nights

• LSST = Super-SDSS: an optical/near-IR survey of the ob-

servable sky in multiple bands (grizY) to V∼26.5 (coadded)

Science Drivers• Dark Energy and Dark Matter (through weak lensing, SNe Ia,

clusters)

• The Milky Way Map (main sequence to 150 kpc, RR Lyrae to

400 kpc, parallaxes for all stars within 500 pc)

• The Solar System Map (over a million main-belt asteroids,

∼100,000 KBOs, Sedna-like objects to beyond 150 AU)

• The Transient Universe (a variety of time scales ranging from

∼10 sec, to the whole sky every 3 nights)7

• Immediate public data distribution (transients within 30 sec)

• 30 TB of data per night (3.2 Gpix camera ∼27 SDSS cameras)

• 60 PB of data over ten years

• A collaboration of numerous (∼20) US institutions (NOAO, Re-

search Corporation, UA, UW, . . . JHU, Harvard, . . . DoE Labs,

. . . Google, Microsoft, . . . )

• A combination of government (NSF and DoE) and private fund-

ing

• Already underway with significant private and NSF funding

• The first light around 2013

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Photometric ParallaxMethod

• Adopted a single relation that

agrees with geometric paral-

lax measurements for nearby

M dwarfs, and with globular

cluster CMDs.

• To increase signal-to-noise at

the faint end, stars are ML

projected on the stellar locus

• Applied to 50 million stars in

6500 deg2 and 100 pc to 15

kpc distance range

• Pitfalls: systematic errors in

adopted relation (e.g. metal-

licity effects), contamination

by giants, smaller distance

range than for e.g. red giants

and RR Lyrae

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Dissecting Milky Way with SDSS

• Traditional approach: assume initial mass function, fold with

models for stellar evolution; assume mass-luminosity relation;

assume some parametrization for the number density distri-

bution; vary (numerous) free parameters until the observed

and model counts agree. Uniqueness? Validity of all assump-

tions?

• SDSS photometric parallax approach: adopt color-luminosity

relation, estimate distance to each star, bin the stars in XYZ

space and directly compute the stellar number density (for

each narrow color bin). There is no need to a priori assume,

the number of, and analytic form for Galactic components

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Local maps: thin disk• Red(ish) stars have small lumi-

nosity: sampled to a few kpc

• The maps are roughly consistent

with an exponential disk out to

∼1 kpc: the lines of constant

density are straight lines

• The slope of these lines is given

by the ratio of exponential scale

height and scale length

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Thin to thick disk transition• Yellow(ish) stars have interme-

diate luminosities: sample the

transition from thin to thick disk

at a few kpc

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Is the Thick Disk ReallyNeeded?

• It is not needed to fit ρ(Z|R =

R�: an appropriate halo, with a

thin disk, can explain the counts

• However, when stars are sepa-

rated by metallicity (using u − g

color as a proxy), low-metalicity

stars follow the halo component,

and the density profile for high-

metalicity stars requires the thick

disk component (or, at least, a

profile different from a single ex-

ponential disk)

• A more robust estimate of the

required number of components

could be obtained by the full

2D analysis of the density maps,

but. . .

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Large Virgo Overdensity

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Summary

• 3D stellar number density maps of the Milky Way from SDSSphotometric observations of ∼50 million stars

• A two-component exponential disk model is in fair agreementwith the data

• Halo properties poorly constrained due to rich substructureand limited sky coverage; however, an oblate halo is alwayspreferred (no strong evidence for triaxial halo)

• A remarkable localized overdensity in the direction of Virgoover ∼1000 deg2 of the sky

• Clumps/overdensities/streams are an integral part of MilkyWay structure, both of halo and the disk(s)

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The SDSS Kinematics Data

• SDSS has already obtained over 200,000 stellar spectra, withradial velocities accurate to ∼10 km/s

• SDSS-POSS proper motions (50 yrs baseline), limited by thePOSS astrometric accuracy (0.15 arcsec, recalibrated POSSastrometry by Sesar et al. and Munn et al.), resulting inproper motion accuracy of ∼3 mas/yr; usable to g ∼ 20

• SDSS-SDSS proper motions (∼5 yrs baseline) accurate to∼6 mas/yr (using only 2 epochs); usable to g ∼ 21

• SDSS-LSST proper motions (∼15 yrs baseline, limited by theSDSS astrometry) accurate to <1 mas/yr; usable to g ∼ 22

SDSS is revolutionizing kinematic studies of the Galactic struc-ture (3 mas/yr corresponds to 15 km/s at 1 kpc, and radialvelocities are measured out to 10 kpc)

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The Milky Way Kinematics Studies Enabled by

SDSS

• For a large number of stars, spread with large baseline over

the sky, SDSS has measured all three velocity components

• By selecting different directions on the sky, systematics can

be reliably controlled (in addition to e.g. using quasars to

determine proper motion errors)

• The dependence of velocity on position, vφ(X, Y, Z), vR(X, Y, Z),

and vZ(X, Y, Z), can be studied directly

• One can also tag the stars by e.g. metallicity (using the u−g

color as a proxy) and study the metallicity-kinematics corre-

lations (with 10-100 times larger sample than previously)

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Photometrically and Astrometrically Variable Ob-

jects

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360180

0

4,859 (halo) stars with 0.75 < u-g < 0.9

180

-400 -200 0 200 400

radial velocity (km/s)

Radial velocities for starswith u− g excess

• u − g < 1 selects low-

metallicity stars, which are

presumably halo stars

• stars with u− g < 1 show a

strong dipole in the radial

velocity distribution: net

motion relative to the Sun

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360180

0

Halo stars corrected for solar motion (209.5 km/s towards l=78.2 b=-4.4)

180

-400 -200 0 200 400

radial velocity (km/s)

Radial velocities for starswith u− g excess

• u − g < 1 selects low-

metallicity stars, which are

presumably halo stars

• stars with u− g < 1 show a

strong dipole in the radial

velocity distribution: net

motion relative to the Sun

• when solar motion is ac-

counted for, stars with u −g < 1 show no overall mo-

tion: halo is not rotating

(vrot < 10±X km/s, X∼10)

• the velocity dispersion is

large (∼100 km/s)

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Halo vs. Disk(s) Kinematics• Kurucz models indicate that

stars with u − g < 1 have low

metallicity (Z∼-2)

• Stars with u − g < 1 have

markedly different kinematics

than stars with u− g > 1

• Low-metallicity stars have fairly

constant kinematics behavior

within a few kpc

• High-metallicity stars have

smoothly increasing rotational

lag and velocity dispersion

with Z for all three velocity

components

• The dependence of the rota-

tional lag on the height above the

plane dominates over the radial

gradient

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Halo vs. Disk(s) Kinematics• When looked in detail, kinematic

structure is more complex than,

e.g., Schwarzschild (Gaussian)

velocity distribution, and the nor-

malization of individual compo-

nents is inconsistent with rela-

tive normalization inferred from

counts

• Standard kinematic models can-

not explain the data.

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Grand Summary

• Clumps/overdensities/streams are an integral part of Milky

Way structure, both of halo and the disk(s)

• Analogous complexity is seen in the Milky Way kinematics

SDSS is revolutionizing studies of the Galactic structure, and

Pan-STARRS and LSST will do even better!

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