galaxy formation, theory and modelling shaun cole (icc, durham) 25 th october 2007 icc photo:...
TRANSCRIPT
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)
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 )|()()(
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
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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
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
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