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  GRB, SNGRB, SN and and identification of the identification of the

hostshostsValentina Grieco

by means by means ofof

evolution modelsevolution modelschemicalchemical

Trieste, 28 nov. 2013

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Outline

A brief introduction of the SN-GRB connection Chemical evolution of galaxies of different

morphological type (elliptical, spiral, irregular) with dust

Local and cosmic rates in the Universe (SFR,GRB)

Comparison between model results and observed abundance patterns in GRB hosts:

identification of the host nature on the basis of abundances and abundance ratios

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SN Ib/c and Long-GRBSN Ib/c and Long-GRB

Long GRBs have been associated to SNe Ib/c SNe Ib/c originate from the explosion of very massive stars suffering strong mass loss. Metallicity effect in stellar evolution are quite important.

Studying SN Ib/c rates in galaxies of different morphological type helps to put constrains on the nature of LGRBs and on the evolution of galaxies

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Central engine emitingrelativist shell of plasma

Differences in the velocity field create internal shocks

Interaction between the shells andthe ISM create external shocks

Collapsar Model

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Methodology: Rates

AIM: Supernova Ib/c rates (Ell.-Spir.-Irr.)

SFR, Z evo

modelsmodelsChemical evolution

models

Local Universe:SN Ib/c rates + Z effect

Cosmic Universe:• CSFR = Σk ψk(t) nk

*

• CSNR• Comparison with RGRB

and Swift data

of elliptical, spiral and irregular galaxies

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Methodology:Host identification

SFR, [X/Fe],

Mdust

modelsmodelsChemical evolution models with dust

Local Universe: • nature of GRB Host Galaxy• chemical age determination

Cosmic Universe:• Sample of Ghost galaxies• Cosmic dust rate

of elliptical, spiral and irregular galaxies

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Basic ingredients of galaxy Basic ingredients of galaxy evolutionevolution

Initial conditions:

The stellar birthrate function: SFR, IMF

The stellar yields

Gas flows: outflow,

Models for spirals, ellipticals and irregulars

open or closed-box, initial chemical composition

Infall ( )amount of IS gas turning into stars per unit time

distrib. of stars as a function of stellar mass

tgas

eAdt

dM

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Star formation rate

the occurrence of galactic wind stop

the SF

Chomiuk & Povich, 2011

• Harris & Zaritsky, 2009

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Single stars Binary systems

The computation of SN Ib/c The computation of SN Ib/c RateRate

Wolf-Rayet stars Close binary systems

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100

/ )()()( dmmFdmmtSNRsun

WR

M

McbI

where:• MWR = 25 M⊙ (constant) or MWR = M(Z)• F 0.15 fraction of massive binary stars producing Sne Ib/c

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Massive stars, mass Massive stars, mass loss, metallicity and loss, metallicity and

SNRSNRIb/cIb/c

Mass loss in massive stars depends on the initial stellar mass and its metallicity Z

The mass loss influences the minimum mass of stars forming Wolf-Rayet stars (MWR): the higher is Z and conseguently the mass loss rate, the lower is the initial mass of WR

Z MLoss(Z,Mini) MWR SNR

Nota: We assume a rel. MWR-Z from recent models of Georgy et al 2009

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Evolution Z, MEvolution Z, MWRWR vs Time vs Time

MWR – Z rel. by Georgy et al. (2009)

Ell

Spir

Irr

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Predicted and observed SN Ib/c Predicted and observed SN Ib/c rate + GRB rate in the local rate + GRB rate in the local

UniverseUniverse

GRB Rate

SNR Spir

SNR Irr

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SNIbc/SNII

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Cosmic star formation rate Cosmic star formation rate (CSFR)(CSFR)

k = galaxy type n* = galaxy number density

compilation of data provided by Hopkins (2004) best fit of data by Cole et al. (2001) Strolger (2004) , Steidel (1999) – turquoise, orange line Porciani & Madau (2001), Menci et al.(2004) – violet, blue line

Assumptions:•All galaxies started forming stars at the same time•No number density evolution•zf = 10

Consequence:•High peak in CSFR @ high z

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Cosmic Star formation Cosmic Star formation ratesrates

compilation of data provided by Hopkins (2004)

best fit of data by Cole et al. (2001)

Strolger (2004) , Steidel (1999) – turquoise, orange line

Porciani & Madau (2001), Menci et al.(2004) – violet, blue line

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Cosmic SNR, Cosmic SNR, RRGRBGRB CSNR CSFR

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Cosmic SNR, RCosmic SNR, RGRB GRB : : Ghirlanda et Ghirlanda et

al. 2013al. 2013Rgrb/Rsn = 0.3%

Complete Sample of simulted grb

Grey Dashed line: RGRB without number density evolution

Rgrb pointing to us

Rgrb of the Swift sample

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The effect of metallicity on The effect of metallicity on CSNRCSNR

CSFR by Cole et al. (2001)

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GRB Host galaxy

S

Ca

Si

Mg

Ni Zn

O

Models for spirals, ellipticals and irregulars

GRB Host

identification

Verify the models prediction using obs. constrains

SFR, Mstar, Mz, Mgas, Z, Av etc …

First constrainof the models

ObservationalAbundances

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How do the stars enrich the ISM ?

Massive stars (M > 8 Msun): explode as core-collapse Supernovae (Woosley & Weaver 95) (O, Si, Mg)

A fraction A (~10%, Matteucci et al. 06) of all the

stars in binary systems with mass (3 M/Msun 16): explode as type Ia SNe (Nomoto et al. 97) (Fe,Si)

Low and Intermediate mass stars (0.8< M/Msun <8): stellar winds (van den Hoeck & Groenewegen 97), (C, N)

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Assumptions about dust (Dwek, 1998; Calura & al. 2008)

The main refractory elements are:

C, O, Mg, Si, S, Ca and Fe

We assume two different types of grains:

- silicate dust: O, Mg, Si, S, Ca, Fe

- carbon dust: C

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Dust processes: production…

The condensation efficiency (analogous to the stellar yields) for

the dust producers are: SW, SNIa, SNII (Dwek 1998)

Dust producers: i) Low and intermediate mass stars, LIMS :

dust is produced during the AGB phase

Note: the dust formation depends on the composition of stellar envelopes (in particular O,C)

ii) SNII

iii) SNIa

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… destruction and accretion

is primarily do to the propagation of SN shock waves in the

warm/ionized ISM; for a given element i the destruction

timescale is:

Dust accretion:

MSNR=mass of the IS gas swept up by SN remnant

MSNR1300 Msun (Dwek et al. 2007)

occurs in dense molecular cloud (Dwek 98, Inoue 2003) where volatile elements can condensate onto pre-existing grain cores; for a given element i the accretion timescale is:

with (0,i 5 x 107 yr) ,

Gi=Mgas Xi(t)/Mtot

Dust destruction:

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Chemical evolution equation for the dustXdust,i(t): abundance by mass of the element i in the dustG(t): ISM fraction at the time tGdust,i(t): normalised mass density of element i at time t in the dust

IMFSFR

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the condensation efficiencies of the element i in stellar winds,type Ia SNe, and type II SNe.These quantities represent the fractions of the element i which is condensed into dust and restored into the ISM by low and intermediate mass stars, type Ia SNe, and type II SNe, respectively.

the dust destruction and accretion rates. These terms depend on τdestr and τaccr, which represent the typical timescales for destruction and accretion, respectively

accounts for possible ejection of dust into the IGM by means of galactic winds

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The basic idea

We use a chemical evolution model with continuous SFwhere the main parameter is the Star Formation Efficiency

Is it possible to constrain the nature of galaxies mainly by meansof the comparison with the observed abundance ratios [X/Y] ?

Work in progress… Apply the method to a large sample of GRB hosts: Are the GRB occurring preferentially in low Z environment? Are the GRB good star forming tracers at high redshift?

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The basic idea

1) Comparison of abundance data (first obs. constrain): fix the model for each GRB host and use the code’s

output (SFR, stellar mass, Mgas/MZ, evolution of the elements as a function of time, etc etc)

Find other constrains: photometric GRB host data If there is no info on SFR and Av we can obtain Av from our models :

2) Age determination: from zGRB to zgalaxy

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Starting point

The model for irregulars has a mass in stars of 109 Msun and SFE of 0.1Gyr-1

The spiral has 5 1010 Msun and SFE of 1 Gyr-1

The elliptical has 1011 Msun and SFE of 10 Gyr-1

All the models form by gas accretion but on differenttimescales: faster in spheroids and slower in dwarf irregulars

Galactic winds are considered : Eth(ISM) > Ebind(GAS)

Constraints: the models have to reproduce the mainproperties of local galaxies

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alpha element and time delay model

Ref. Matteucci 2001

Alpha/Fe vs FE/H depend on the SFH of galaxy

Aplha/Fe SNII/SNIa

Plateau: SNIICut: onset of the SNIa explosion

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D’Elia et al. 2013 in prep. GRB120327A @ z = 2.81

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D’Elia et al. 2011 GRB081008 @ z = 1.97

[Zn/Fe]: inversion of the models prediction > SFE, > dust grain destruction SO irregulars (lower SFE) predict higher abundances

Refractory elements

O,Mg,S,Si,Fe,Ca,Ni

Zn no refractory

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D’Elia et al. 2011 - GRB081008 @ z = 1.97

Models with dust prescriptions Models without dust

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Kruhler et al 2013 – GRB120815 @ z = 2.36

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Age determination

We derive also the chemical age of each object, namely the time necessary to produce the observed abundance ratios

Knowing the redshift of the object and the chemical age we can derive the redshift of formation.

Our results show that all the GRB host are young:

- Age(120327A) = 50 Myr - Age (081008) = 0.32 Gyr - Age (120815) = 15 Myr

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Good agreement between observed and predicted Type Ib/c SN rates only assuming both single WRs and massive binaries as progenitors

By adopting the cosmic SFR derived from backward models we predict a higher SFR at high redshift respect to the hierarchical scenarios

The metallicity effect is evident only in the early galactic evolutionary stages

From the comparison between the LGRB and the SN Ib/c rates, we derived a ratio of ~ 3 ∙ 10-3 M⊙

(only a fraction of these SNe gives rise to GRBs)

Summary

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SummaryThe GRB081008 is probably hosted in a spiral

although O is too low. The estimated age is 50MyrThe GRB120327 is hosted by a spheroid with very

intense star formation. The estimated age is 0.32 Gyr

The GRB 120815, seems to belong to an elliptical galaxy. The estimated age is 0.15 Gyr

The effects of dust in chemical models are in some cases quite strong, especially for ratios non-refractory/refractory

The result we found are important because previous studies had always suggested dwarf irregular to be the host of grb

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Future work

Update the dust prescriptions and test different assumptions about the mechanisms of production, destruction, accretion

Testing the model in the SN to constrain our assumptions by means of a comparison with the observational data

Collect more data on GRB hosts and apply the method to a large sample using also the photometric GRB host data available

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SFR-Av relation

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Age determination