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Galaxy Formation, Theory and Modelling

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- Shaun Cole (ICC, Durham)

Collaborators:

Geraint Harker

John Helly

Adrian Jenkins

Hannah Parkinson

ICC Photo: Malcolm Crowthers

25thOctober 2007

- 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

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

Lacey & Cole (1993)

- Lacey & Cole trees (extended Press-Schechter)
- Simulation from the Virgo Aquarius project
- Parkinson, Cole and Helly trees

Lacey & Cole (1993)

- Millennium Simulation (movie and merger trees)
- Lacey & Cole trees
- Parkinson, Cole and Helly trees

Lacey & Cole (1993)

- Lacey & Cole trees (extended Press-Schechter)
- Simulation from the Virgo Aquarius project
- Parkinson, Cole and Helly trees

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

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

NFW profiles, but with what concentration

Neto et al 2007

- 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)

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)

Cole et al 2000

- Rees-Ostriker/ Binney cooling argument cannot produce M* break
- Feedback needed at faint end

Benson & Bower 2003

- 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

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Stars

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Star Formation Rate and Metallicity as a Function of Time + IMF assumption

Library of Stellar Spectra

Convolution Machine

Dust Modelling

Galaxy SED

Maraston 2005

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

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

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

Two grids of models with

and varying

Achieved by rescaling particle masses and velocities (Zheng et al 2002)

-- Grid 1

-- Grid 2

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.

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

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

- 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.

High values still

Generally preferred.

The constraint on

from b-band 2dFGRS data

Norberg 2002+

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

More modelling work required.

- 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