Simulations of the galaxy population
constrained by observations from
z=3 to present day: implications for
galactic winds and fate of their ejectaBruno Henriques
Simon White, Peter Thomas, Raul Angulo,
Qi Guo, Gerard Lemson, Volker Springel
MotivationMotivation
Because we should. The physics of galaxy formation are complex but observations suggest they must obey simple relations.
Why use a phenomenological approach to study galaxy formation?
Still, we do not have a good understanding and cannot work from first principles, so models must be
observationally based.
Fast method to compute the evolution of the galaxy population across cosmic time for samples as large as modern surveys.
The evolution of the stellar mass function
The model fits the present day distribution of masses but predicts the dwarf galaxy population to build up too early.
Guo2011Guo2011
Massive galaxies are too blue and dwarf galaxies too red
Dwarf galaxies are too old and are not forming enough stars
Dwarf galaxies are too clustered
clustering
colour
SSFR
Semi-analytic modelling
MCMC
Complex galaxy
formation physics
Choose parameters to sample
Star formation, SN feedback, AGN feedback efficiency, Metals yield
3. Self-consistent model of galaxy 3. Self-consistent model of galaxy formation across cosmic timeformation across cosmic time
Henriques et al. (2009), Henriques & Thomas (2010), Henriques et al. (in prep.)
Large Volume
Across Cosmic Time
Robust statistical method to explore the allowed likelihood regions in high-dimensional
parameter spaces
Constrain the model at multiple redshifts
Stellar Mass Function, K-band & B-band Luminosity Functions
Wide and narrow surveys combined to
achieve good statistics and large dynamical range.
Maximum and minimum
observational errors used to estimate
systematic uncertainties.
Time varying parametersTime varying parameters
Reincorporation of gas after ejection by SN feedback needs
to increase towards low redshift
All other parameters have consistent regions at all
redshifts
Reincorporation time scaling with Mvir, similar to Oppenheimer et al. (2008, 2010)
Strong ejection + no reincorporation set the low mass end at high-z
Strong reincorporation at later times produces the required build up for z<1
Colors and SFRColors and SFRThe delayed reincorporation of gas shifts star formation in dwarfs
to lower redshifts.
Low mass galaxies have higher star formation rates
and younger ages.
A population of low mass galaxies with blue colours remains down to z=0
Satellite galaxies in massive halos have lower
mass, hence reducing clustering at fixed mass
Galaxy formation physics, and not just
cosmology/merging, have a strong impact on galaxy
clustering.
ConclusionsConclusions
MCMC methods can be used to learn exactly how specific descriptions of a physical process affect galaxy observables at different epochs in a self-consistent way. The allowed likelihood regions in parameter space can be explored for any combination of observations at multiple epochs.In order to explain the observed evolution of the number density of intermediate/low-mass galaxies, the reincorporation of ejected gas should scale approximately with Mvir, being negligible at low mass at z>2 and rapid for most galaxies at low redshift.Low-galaxies form later and are significantly younger at z=0
Evolution of the massive end is reproduced across all redshifts
Phenomenological models provide a fast method to describe the formation and evolution of galaxies in a cosmological volume, with high resolution and across cosmic time.
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No feedback
The halo mass function is much steeper at both ends than the galaxy stellar mass
function
Supernova feedback has the right scale to make star formation sufficiently inefficient in small haloes
The reheated gas would eventually cool in massive haloes producing an excessive
number of bright galaxies
Luminosity Function
low mass
Observations
Observations
Supernova feedback
high mass
high mass low mass
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Massive galaxies have more gas fuel than small ones
No ongoing star formationOlder populations than small galaxies
Z=2.0
Z=1.0
Z=0.0
Stars
Cold Gas
Hot Gas
Ejected Gas
RecyclingStar
Formation
CoolingReheating
Ejection
Reincorporation
Stars
The Munich ModelThe Munich Model
different supernova feedback (increased efficiency)
Merger treatment
1.The Munich Model1.The Munich Model
Guo et al. 2011
Henriques et al. 2011, 2012
different stellar populations
Croton et al. 2006
De Lucia & Blaizot 2007
AGN feedback model (suppression of cooling)
dust model
SN feedback model - reheating + ejection + reincorporation
Extended MCMC CapabilitiesExtended MCMC Capabilities
Observational constraints at multiple redshifts
Time-evolution of parameters (pre-processing step)
Stellar mass and luminosity functions constraints from z=3 to z=0 Takes full advantage of the self-consistent evolution of galaxies
If not needed, the current parametrisation is not ruled out by observationsIf needed, a different parametrisation is required (it rules out any others)
If a good fit can not be found, the current model is ruled out
M05 vs BC03
Gas
TB-AGB
TB-AGB + RHeB
Web-based, modeler & observer friendly semi-analytic model
Combine the most robust set of dark matter numerical simulations availableStellar Mass resolution of 108M with a large enough volume to sample BAO
MS, MII & MXXL
Monte Carlo Markov Chain optimization +
Fit physical and cosmological parameters
Modular implementation of the physics
“Observer friendly” outputs Choose IMF, SPS, Bands, Dust model
GALFORMODGALFORMOD
Chemical EnrichmentChemical Enrichment
0.8 M 8 M
SN Ia + Stellar Winds SN II
Metals return timescale<100 Myr
Rob Yates, Peter Thomas, Simon White, Guinevere Kauffmann,
Bruno Henriques
Far-Infrared EmissionFar-Infrared EmissionPeter Thomas, Sorour Shamshir, Bruno Henriques, Qi Guo +
Sussex Infrared
Use empirical templates from Herschel to get an emission spectra for the light re-emitted by dust
Full radiative transfer code
Cosmology Sampling with MCMCCosmology Sampling with MCMC
Incorporate the Angulo & White 2010 formalism into the semi-analytic model.
Include cosmological parameters in the sampling.
Bruno Henriques, Marcel Van Daalen, Raul Angulo, Simon
White, Volker Springel, Fabio Fontanot, Qi Guo
ColoursColours
Somerville
Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392)
Light – Weighted Ages Mass – Weighted Ages
TP-AGB
M05
M05BC03
Average!!!
Ages of GalaxiesAges of Galaxies
SSP
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Van der Wel, Franx, Wuyts, et al. 2006
Chandra Deep Field - South
ACS+IRAC+J&H filters
Older then the Universe! Undetected in MIPS!
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Maraston, Daddi, Renzini, et al. 2006
What are the implications for galaxy formation models?
MUSYC – Gawiser et al. 2006GOODS – Giavalisco et al. 2004
Optical to mid-infrared data
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Marchesini 2009Marchesini 2009
CB07
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BC03
CB07
M05
Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392)
