Bruno Henriques Simon White, Peter Thomas, Raul Angulo, Qi Guo, Gerard Lemson, Volker Springel

1. Simulations of the galaxy population constrained by observations from z=3 to present day: implications for galactic winds and fate of their ejecta Bruno Henriques Simon White, Peter Thomas, Raul Angulo, Qi Guo, Gerard Lemson, Volker Springel

2. Motivation Why use a phenomenological approach to study galaxy formation? Because we should. The physics of galaxy formation are complex but observations suggest they must obey simple relations. Still, we do not have a good understanding and cannot work from first principles, so models must be observationally based. Fast method to compute the evolution of the galaxy population across cosmic time for samples as large as modern surveys.

3. Guo2011 The evolution of the stellar mass function The model fits the present day distribution of masses but predicts the dwarf galaxy population to build up too early.

4. colour Dwarf galaxies are too clustered clustering Massive galaxies are too blue and dwarf galaxies too red SSFR Dwarf galaxies are too old and are not forming enough stars

5. 3. Self-consistent model of galaxy formation across cosmic time Henriques et al. (2009), Henriques & Thomas (2010), Henriques et al. (in prep.) Complex galaxy formation physics Semi-analytic modelling MCMC Large Volume Robust statistical method to explore the allowed likelihood regions in high-dimensional parameter spaces Across Cosmic Time Choose parameters to sample Star formation, SN feedback, AGN feedback efficiency, Metals yield

6. Constrain the model at multiple redshifts Wide and narrow surveys combined to achieve good statistics and large dynamical range. Maximum and minimum observational errors used to estimate systematic uncertainties. Stellar Mass Function, K-band & B-band Luminosity Functions

7. Time varying parameters Reincorporation of gas after ejection by SN feedback needs to increase towards low redshift All other parameters have consistent regions at all redshifts Reincorporation time scaling with Mvir, similar to Oppenheimer et al. (2008, 2010)

8. Strong ejection + no reincorporation set the low mass end at high-z Strong reincorporation at later times produces the required build up for z<1

9. The delayed reincorporation of gas shifts star formation in dwarfs to lower redshifts. Colors and SFR A population of low mass galaxies with blue colours remains down to z=0 Low mass galaxies have higher star formation rates and younger ages.

10. Satellite galaxies in massive halos have lower mass, hence reducing clustering at fixed mass Galaxy formation physics, and not just cosmology/merging, have a strong impact on galaxy clustering.

11. Conclusions Phenomenological models provide a fast method to describe the formation and evolution of galaxies in a cosmological volume, with high resolution and across cosmic time. MCMC methods can be used to learn exactly how specific descriptions of a physical process affect galaxy observables at different epochs in a self-consistent way. The allowed likelihood regions in parameter space can be explored for any combination of observations at multiple epochs. In order to explain the observed evolution of the number density of intermediate/low-mass galaxies, the reincorporation of ejected gas should scale approximately with Mvir, being negligible at low mass at z>2 and rapid for most galaxies at low redshift. Low-galaxies form later and are significantly younger at z=0 Evolution of the massive end is reproduced across all redshifts

12. low mass Luminosity Function No feedback The halo mass function is much steeper at both ends than the galaxy stellar mass function Observations low mass high mass Supernova feedback has the right scale to make star formation sufficiently inefficient in small haloes Supernova feedback The reheated gas would eventually cool in massive haloes producing an excessive number of bright galaxies Observations high mass 14 September 2014 12

13. Z=2.0 Z=1.0 Z=0.0 Massive galaxies have more gas fuel than small ones No ongoing star formation Older populations than small galaxies 14 September 2014 13

15. The Munich Model Stars Reincorporation Hot Gas Reheating Cooling Stars Ejected Gas Star Formation Recycling Cold Gas Ejection

16. Croton et al. 2006 1.The Munich Model AGN feedback model (suppression of cooling) De Lucia & Blaizot 2007 dust model SN feedback model - reheating + ejection + reincorporation Guo et al. 2011 different supernova feedback (increased efficiency) Merger treatment Henriques et al. 2011, 2012 different stellar populations

18. Extended MCMC Capabilities Observational constraints at multiple redshifts Stellar mass and luminosity functions constraints from z=3 to z=0 Takes full advantage of the self-consistent evolution of galaxies Time-evolution of parameters (pre-processing step) If not needed, the current parametrisation is not ruled out by observations If needed, a different parametrisation is required (it rules out any others) If a good fit can not be found, the current model is ruled out

19. M05 vs BC03

20. Gas

21. TB-AGB TB-AGB + RHeB

22. GALFORMOD Web-based, modeler & observer friendly semi-analytic model Combine the most robust set of dark matter numerical simulations available Stellar Mass resolution of 108M with a large enough volume to sample BAO MS, MII & MXXL Monte Carlo Markov Chain optimization + Fit physical and cosmological parameters Modular implementation of the physics “Observer friendly” outputs Choose IMF, SPS, Bands, Dust model

23. 0.8 M SN Ia + Stellar Winds SN II Chemical Enrichment 8 M Metals return timescale <100 Myr Rob Yates, Peter Thomas, Simon White, Guinevere Kauffmann, Bruno Henriques

24. Far-Infrared Emission Peter Thomas, Sorour Shamshir, Bruno Henriques, Qi Guo + Sussex Infrared Use empirical templates from Herschel to get an emission spectra for the light re-emitted by dust Full radiative transfer code

25. Cosmology Sampling with MCMC Bruno Henriques, Marcel Van Daalen, Raul Angulo, Simon White, Volker Springel, Fabio Fontanot, Qi Guo Incorporate the Angulo & White 2010 formalism into the semi-analytic model. Include cosmological parameters in the sampling.

26. Colours

27. Somerville

28. Ages of Galaxies Light – Weighted Ages Mass – Weighted Ages BC03 M05 Average!!! M05 TP-AGB Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392)

29. SSP Van der Wel, Franx, Wuyts, et al. 2006 Chandra Deep Field - South ACS+IRAC+J&H filters 14 September 2014 29

30. Maraston, Daddi, Renzini, et al. 2006 Older then the Universe! Undetected in MIPS! What are the implications for galaxy formation models? 14 September 2014 30

31. Marchesini 2009 Optical to mid-infrared data GOODS – Giavalisco et al. 2004 MUSYC – Gawiser et al. 2006 14 September 2014 31

32. CB07 M05 CB07 BC03 Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392) 14 September 2014 32