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)