Download Galaxy Formation, Theory and Modelling

Galaxy Formation, Theory and
Modelling

Shaun Cole (ICC, Durham)
Collaborators:
Geraint Harker
John Helly
Adrian Jenkins
Hannah Parkinson
25th October 2007
ICC Photo: Malcolm Crowthers
Outline
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An Introduction to the Ingredients of Galaxy
Formation Models
Recent improvements/developments
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(Parkinson, Cole & Helly 2007)
Modelling Galaxy Clustering
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Dark matter merger trees
Constraints on s8
Conclude
(Harker, Cole & Jenkins 2007)
Galaxy Formation Physics
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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 PressSchechter)
• 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 PressSchechter)
• 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
Parkinson, Cole and Helly
2007
Insert an empirically motivated factor into this merger rate equation
G0  0.61,  1  0.27,  2  0.0
Sheth-Tormen or Jenkins
universal mass function is a
good fit to N-body results at
all redshifts.
Thus we require:
f ST ( M )   f ST (m) FPCH (m | M )dm
f ST (m)   f ST ( M ) f PCH (m | M )dM
Very nearly
consistent with the
universal ShethTormen/Jenkins
Mass Function
   ( z ) / s ( m)
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
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Standard Assumptions:
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Gas initially at virial temperature
with NFW or bmodel profile
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All gas within cooling radius cools
Improved models being
developed (McCarthy et al):
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Initial power law entropy
distribution
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Cooling modifies entropy and
hydrostatic equillibrium determines
modified profile.
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Explicit recipe for shock heating
Helly et al. (2002)
Galaxy mergers
Galaxy orbits decay due to
dynamical friction
• Lacey & Cole (1993)
– Analytic
– Point mass galaxies
– Orbit averaged quantities
t DF  0.5 f ( )VC rc2 / CGm ln( )
• Jiang et al 2007 (see also
Boylan-Kolchin et al 2007)
Star formation and feedback processes
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Rees-Ostriker/
Binney cooling
argument cannot
produce M* break
Cole et al 2000
Feedback needed
at faint end
Benson & Bower 2003
AGN formation and feedback processes
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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
Library of Stellar Spectra
✶ ✶ ✶
✶
Rate
✶ Star Formation
Stars
✶ ✶
and
✶ Metallicity as a
Function
✶ of✶Time +
IMF assumption
Convolution Machine
Dust Modelling
Galaxy SED
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 TPAGB stars can make
a dominant
contribution in the
NIR.
Maraston 2005
Semi-analytic Modelling
DM and Gas
density profile
Dark Matter
Merger Trees
Gas cooling
rates
Galaxy merger
rates
Semi-Analytic
Model
Luminosities,
colours
Morphology
Star formation,
feedback, SPS
Positions and
velocities
Structure &
Dynamics
Star formation
rate, ages,
metallicities
Semi-analytic + N-body Techniques
Harker, 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.
5123 Particles in 300 Mpc/h
box
Benson
Harker, Cole & Jenkins 2007
Two grids of models
with
 s 8  0.8(0.3)
0.5
 s 8  0.9(0.3)
0.5
0.5
0.5
-- Grid 1
-- Grid 2
and varying 
Achieved by rescaling
particle masses and
velocities (Zheng et
al 2002)
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.
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 s 8.
Clustering strength
primarily
dependent on s 8
I.E. Galaxy bias
predicted by the
GALFORM
model is largely
independent of
model details.
s8
s8
The constraint on s 8
s8
 s 8  0.97  0.06
s8
 s 8  0.97  0.06
How Robust is this constraint?
• For this dataset the error on s 8 (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) s 8  0.75  0.05
• If the method is robust we should get consistent
results for datasets with different luminosity and
colour selections.
The constraint on
s8
from b-band 2dFGRS data
High values still
Generally preferred.
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.
Conclusions
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Significant improvements in our understanding and
ability to model many of the physical processes
involved in galaxy formation have been made in
recent years.
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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.
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Comparisons with extensive survey data can place
interesting constraints on galaxy formation models and/or
cosmological parameters