Galaxy Formation, Theory and Modelling

Shaun Cole (ICC, Durham)

25th October 2007 ICC Photo: Malcolm Crowthers

Collaborators:

Geraint HarkerJohn HellyAdrian JenkinsHannah 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

0.0,27.0,61.0 210 G

Parkinson, Cole and Helly 2007

Very nearly consistent with the universal Sheth-Tormen/Jenkins Mass Function

dMMmfMfmf PCHSTST )|()()(

Sheth-Tormen or Jenkins universal mass function is a good fit to N-body results at all redshifts.Thus we require:

)(/)( mz

dmMmFmfMf PCHSTST )|()()(

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 mergersGalaxy 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)

)ln(/)(5.0 2 CGmrVft cCDF

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 SEDDust 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 2005Maraston 2005

Semi-analytic ModellingSemi-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-analyticSemi-analytic + N-body Techniques+ N-body TechniquesHarker, 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

3512

Benson

Harker, Cole & Jenkins 2007

Two grids of models with

and varying

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

5.08

5.0

5.08

5.0

)3.0(9.0

)3.0(8.0

-- 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 .

8

8

8

8

8 06.097.08

8

The constraint on

06.097.08

8

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.

8

05.075.08

8The constraint on from b-band 2dFGRS data

Norberg 2002+

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