1. 1 GRB, SN and identification of the hosts GRB, SN and identification of the hosts Valentina Grieco by means of evolution models chemical Trieste, 28 nov. 2013

2. 2 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

3. 3 SN 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

4. 4 Central engine emiting relativist shell of plasma Differences in the velocity field create internal shocks Interaction between the shells and the ISM create external shocks Collapsar Model

5. 5 Methodology: Rates AIM : Supernova Ib/c rates (Ell.-Spir.-Irr.) SFR, Z evo models Chemical evolution models Local Universe : SN Ib/c rates + Z effect Cosmic Universe: CSFR = Σ k ψ k (t) n k * CSNR Comparison with R GRB and Swift data of elliptical, spiral and irregular galaxies

6. 6 Methodology: Host identification SFR, [X/Fe], M dust models Chemical 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

7. 7 Basic ingredients of galaxy evolution  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

8. 8 Star formation rate the occurrence of galactic wind stop the SF  Chomiuk & Povich, 2011 Harris & Zaritsky, 2009

9. 9 Single stars Binary systems The computation of SN Ib/c Rate Wolf-Rayet starsClose binary systems where: M WR = 25 M ⊙ (constant) or M WR = M(Z) F  0.15 fraction of massive binary stars producing Sne Ib/c

10. 10 Massive stars, mass loss, metallicity and SNR Ib/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 (M WR ): the higher is Z and conseguently the mass loss rate, the lower is the initial mass of WR Z M Loss (Z,M ini ) M WR SNR Nota: We assume a rel. M WR -Z from recent models of Georgy et al 2009

11. 11 Evolution Z, M WR vs Time M WR – Z rel. by Georgy et al. (2009) Ell Spir Irr

12. 12 Predicted and observed SN Ib/c rate + GRB rate in the local Universe GRB Rate SNR Spir SNR Irr

13. 13 SN Ibc /SN II

14. 14 Cosmic star formation rate (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 z f = 10 Consequence: High peak in CSFR @ high z

15. 15 Cosmic Star formation rates 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

16. 16 Cosmic SNR, R GRB CSNR  CSFR

17. 17 Cosmic SNR, R GRB : Ghirlanda et al. 2013 Rgrb/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

18. 18 The effect of metallicity on CSNR CSFR by Cole et al. (2001)

19. 19 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 constrain of the models Observational Abundances

20. 20 How do the stars enrich the ISM ? Massive stars (M > 8 M sun ): 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/M sun  16): explode as type Ia SNe (Nomoto et al. 97) (Fe,Si) Low and Intermediate mass stars (0.8< M/M sun <8): stellar winds (van den Hoeck & Groenewegen 97), (C, N)

21. 21 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

22. 22 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

23. 23 … 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: M SNR =mass of the IS gas swept up by SN remnant  M SNR  1300 M sun (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 10 7 yr), Gi=Mgas Xi(t)/Mtot  Dust destruction:

24. 24 Chemical evolution equation for the dust X dust,i (t): abundance by mass of the element i in the dust G(t): ISM fraction at the time t G dust,i (t): normalised mass density of element i at time t in the dust IMF SFR

25. 25 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 τ destr and τ accr, which represent the typical timescales for destruction and accretion, respectively accounts for possible ejection of dust into the IGM by means of galactic winds

26. 26 The basic idea We use a chemical evolution model with continuous SF where the main parameter is the Star Formation Efficiency Is it possible to constrain the nature of galaxies mainly by means of 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?

27. 27 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, M gas /M Z, 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 A v we can obtain A v from our models : 2) Age determination: from z GRB to z galaxy

28. 28 Starting point The model for irregulars has a mass in stars of 10 9 M sun and SFE of 0.1Gyr -1 The spiral has 5  10 10 M sun and SFE of 1 Gyr -1 The elliptical has 10 11 M sun and SFE of 10 Gyr -1 All the models form by gas accretion but on different timescales: faster in spheroids and slower in dwarf irregulars Galactic winds are considered : E th (ISM) > E bind (GAS) Constraints: the models have to reproduce the main properties of local galaxies

29. 29 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: SNII Cut: onset of the SNIa explosion

30. 30 D’Elia et al. 2013 in prep. GRB120327A @ z = 2.81

31. 31 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

32. 32 D’Elia et al. 2011 - GRB081008 @ z = 1.97 Models with dust prescriptions Models without dust

33. 33 Kruhler et al 2013 – GRB120815 @ z = 2.36

34. 34 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

35. 35  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

36. 36 Summary The GRB081008 is probably hosted in a spiral although O is too low. The estimated age is 50Myr The 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

37. 37

38. 38 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

39. 39 SFR-Av relation

40. 40 Age determination