Galaxy formation theory and modelling
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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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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

25thOctober 2007


Outline

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


Galaxy formation physics

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


The hierarchical evolution of the dark matter distribution

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


The hierarchical evolution of the dark matter distribution1

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


The hierarchical evolution of the dark matter distribution2

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


Galaxy formation theory and modelling

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


Parkinson cole and helly 2007

Parkinson, Cole and Helly 2007

Parkinson, Cole and Helly 2007

Insert an empirically motivated factor into this merger rate equation


Galaxy formation theory and modelling

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


The structure of dark matter halos

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

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)‏


Galaxy mergers

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

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


Agn formation and feedback processes

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

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


Stellar population synthesis and dust modelling1

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


Galaxy formation theory and modelling

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


Galaxy formation theory and modelling

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


Harker cole jenkins 2007

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 20071

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.


Galaxy formation theory and modelling

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


Galaxy formation theory and modelling

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.


Galaxy formation theory and modelling

The constraint on


How robust is this constraint

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.


Galaxy formation theory and modelling

High values still

Generally preferred.

The constraint on

from b-band 2dFGRS data

Norberg 2002+


Galaxy formation theory and modelling

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

More modelling work required.


Conclusions

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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