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Galaxy Formation, Theory and Modelling

Galaxy Formation, Theory and Modelling. Shaun Cole (ICC, Durham). Collaborators: Geraint Harker John Helly Adrian Jenkins Hannah Parkinson. ICC Photo: Malcolm Crowthers. 25 th October 2007. Outline. An Introduction to the Ingredients of Galaxy Formation Models

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Galaxy Formation, Theory and Modelling

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

  2. 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 s8 (Harker, Cole & Jenkins 2007) • Conclude

  3. Galaxy Formation Physics Dark Matter • 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 Gas

  4. 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

  5. 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

  6. 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

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

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

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

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

  11. Gas heating and cooling processes within dark matter halos • Standard Assumptions: • Gas initially at virial temperature with NFW or b-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)‏

  12. 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)

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

  14. 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

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

  16. Maraston 2005 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

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

  18. Semi-analytic+ N-body Techniques Harker, Cole & Jenkins 2007 • Usea 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

  19. 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

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

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

  22. 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.

  23. The constraint on

  24. 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.

  25. High values still Generally preferred. The constraint on from b-band 2dFGRS data Norberg 2002+

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

  27. 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

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