Galaxy Formation, Theory and Modelling Shaun Cole (ICC, Durham) 25 th October 2007 ICC Photo: Malcolm Crowthers Collaborators: Geraint Harker John Helly Adrian Jenkins Hannah Parkinson

Outline An Introduction to the Ingredients of Galaxy Formation Models Recent improvements/developments  Dark matter merger trees (Parkinson, Cole & Helly 2007) Modelling Galaxy Clustering  Constraints on    (Harker, Cole & Jenkins 2007) Conclude

Galaxy Formation Physics The hierarchical evolution of the dark matter distribution The structure of dark matter halos Gas heating and cooling processes within dark matter halos Galaxy mergers Star formation and feedback processes AGN formation and feedback processes Stellar population synthesis and dust modelling Dark Matter Gas

The hierarchical evolution of the dark matter distribution Lacey & Cole trees (extended Press- Schechter) Simulation from the Virgo Aquarius project Parkinson, Cole and Helly trees Lacey & Cole (1993)‏

The hierarchical evolution of the dark matter distribution Millennium Simulation (movie and merger trees) Lacey & Cole trees Parkinson, Cole and Helly trees Lacey & Cole (1993)‏

The hierarchical evolution of the dark matter distribution Lacey & Cole trees (extended Press- Schechter) Simulation from the Virgo Aquarius project Parkinson, Cole and Helly trees Lacey & Cole (1993)‏

EPS Merger Trees (Lacey & Cole 1993, Cole et al 2000)

Parkinson, Cole and Helly 2007 Insert an empirically motivated factor into this merger rate equation Parkinson, Cole and Helly 2007

Very nearly consistent with the universal Sheth- Tormen/Jenkins Mass Function Sheth-Tormen or Jenkins universal mass function is a good fit to N-body results at all redshifts. Thus we require:

The structure of dark matter halos NFW profiles, but with what concentration Neto et al 2007

Gas heating and cooling processes within dark matter halos Standard Assumptions:  Gas initially at virial temperature with NFW or  model profile  All gas within cooling radius cools Improved models being developed (McCarthy et al):  Initial power law entropy distribution  Cooling modifies entropy and hydrostatic equillibrium determines modified profile.  Explicit recipe for shock heating Helly et al. (2002)‏

Galaxy mergers Galaxy orbits decay due to dynamical friction Lacey & Cole (1993) –Analytic –Point mass galaxies –Orbit averaged quantities Jiang et al 2007 (see also Boylan-Kolchin et al 2007)

Star formation and feedback processes Rees-Ostriker/ Binney cooling argument cannot produce M* break Feedback needed at faint end Benson & Bower 2003 Cole et al 2000

AGN formation and feedback processes SN feedback not enough as we must affect the bright end AGN always a sufficient energy source but how is the energy coupled Demise of cooling flows Benefits LF modelling as heats without producing stars Bower et al 2006

Stellar population synthesis and dust modelling ✶ Stars ✶ ✶ ✶ ✶ ✶ ✶ ✶ ✶ ✶ Library of Stellar Spectra Star Formation Rate and Metallicity as a Function of Time + IMF assumption Convolution Machine Galaxy SED Dust Modelling

Stellar population synthesis and dust modelling Many Stellar Population Synthesis codes (eg Bruzual & Charlot, Pegase, Starburst99) are quite mature. But they aren’t necessarily complete. Maraston (2005) showed that TP- AGB stars can make a dominant contribution in the NIR. Maraston 2005

Dust No Dust Diffuse Screen Diffuse in Disk Diffuse + Clouds Dust makes galaxies appear fainter, and typically redder Also re-emits absorbed energy at longer wavelengths (dominating the SED at these wavelengths)‏ Dust has been treated with various degrees of sophistication Dust makes galaxies appear fainter, and typically redder Also re-emits absorbed energy at longer wavelengths (dominating the SED at these wavelengths)‏ Dust has been treated with various degrees of sophistication

Semi-analytic Modelling Semi-Analytic Model Dark Matter Merger Trees DM and Gas density profile Gas cooling rates Star formation, feedback, SPS Galaxy merger rates Luminosities, colours Positions and velocities Star formation rate, ages, metallicities Structure & Dynamics Morphology

Semi-analytic+ N-body Techniques Semi-analytic + N-body Techniques Harker, Cole & Jenkins 2007 Use a set of N-body simulations with varying cosmoligical parameters. Populate each with galaxies using Monte-Carlo DM trees and the GALFORM code. Compare the resulting clustering with SDSS observations and constrain cosmological parameters. Particles in 300 Mpc/h box Benson

Harker, Cole & Jenkins 2007 Two grids of models with and varying Achieved by rescaling particle masses and velocities (Zheng et al 2002) -- Grid 1 -- Grid 2

Harker, Cole & Jenkins 2007 For each (scaled) N-body output we have two variants of each of three distinct GALFORM models. 1.Low baryon fraction (Cole et al 2000) 2.Superwinds (Baugh et al 2005 aka M) 3.AGN-like feedback (C2000hib) Each model is adjusted to match the observed r-band LF.

Zehavi et al 2005 Select a magnitude limited sample with the same space density as the best measured SDSS sample. Compare clustering and determine best fit.

Comparison of models all having the same. Clustering strength primarily dependent on I.E. Galaxy bias predicted by the GALFORM model is largely independent of model details.

The constraint on

How Robust is this constraint? For this dataset the error on (including statistical and estimated systematic contributions) is small and comparable to that from WMAP+ estimates. The values do not agree, with WMAP3+ preferring (Spergel et al 2007) If the method is robust we should get consistent results for datasets with different luminosity and colour selections.

The constraint onfrom b-band 2dFGRS data Norberg High values still Generally preferred.

None of the models produce observed dependence of clustering strength on luminosity over the full range of the data. More modelling work required.

Conclusions Significant improvements in our understanding and ability to model many of the physical processes involved in galaxy formation have been made in recent years.  They are not yet all incorporated in Semi-Analytic models Big challenges remain in modelling stellar and AGN feedback Clustering predictions from galaxy formation models can be more predictive and provide more information than purely statistical HOD/CLF descriptions.  Comparisons with extensive survey data can place interesting constraints on galaxy formation models and/or cosmological parameters