evolution and environment

Evolution and environment • The halo model

– Environmental effects in the SDSS– Halo mass vs. local density

• Mark correlations– SDSS galaxies and their environments– Centre-satellite split and galaxy SEDs

• Passive evolution models– Conditional mass function + halo model

predicts nonlinear correlation function

Light is a biased tracer

Not all galaxies are fair tracers of dark matterTo use galaxies as probes of underlying dark matter distribution, must understand ‘bias’

How to describe different point processes which are all built from the same underlying distribution?

THE HALO MODEL

Environmental effects• In hierarchical models, close connection

between evolution and environment (dense region ~ dense universe ~ more evolved ~ more massive halos ~ more clustering)

n(m|) = [1+b(m)n(m)

• Observed correlations with environment test hierarchical galaxy formation models

Halo-model of galaxy clustering• Two types of pairs: only difference from dark matter

is that number of pairs in m-halo is not m2

• ξdm(r) = ξ1h(r) + ξ2h(r)

• Spatial distribution within halos is small-scale detail

Halo-model of un-weighted correlations

Write 1+ξ = DD/RR as sum of two components:

ξ1gal(r) ~ ∫dm n(m) g2(m) ξdm(m|r)/gal2

ξ2gal(r) ≈ [∫dm n(m) g1(m) b(m)/gal]2 ξdm(r) ≈ bgal

2 ξdm(r)

g2(m) is mean number of galaxy pairs in m-halos (= m2 for dark matter)g1(m) is mean number of galaxies in m-halos (= m for dark matter)

Satellite galaxy counts ~ Poisson

• Write g1(m) ≡ ‹g(m)› = 1 + ‹gs(m)›• Think of ‹gs(m)› as mean number of satellite

galaxies per m halo• Minimal model sets number of satellites as

simple as possible ~ Poisson: • So g2(m) ≡ ‹g(g-1)› = ‹gs (1+gs)› = ‹gs› +

‹gs2› = 2‹gs› + ‹gs›2 = (1+‹gs›)2 - 1

• Simulations show this ‘sub-Poisson’ model works well (Kravtsov et al. 2004)

Two approaches …• Halo Occupation Distribution

(Jing et al., Benson et al.; Seljak; Scoccimarro et al.)– Model Ngal(>L|Mhalo) for range of L (Zehavi et al.; Zheng et

al.; Berlind et al.; Kravtsov et al.; Conroy et al.; Porciani, Magliochetti; Collister, Lahav)

– Differentiating gives LF as function of Mhalo

(Tinker et al., Skibba et al.):

• Conditional Luminosity Function (Peacock, Smith): – Model LF as function of Mhalo , and infer HOD (Yang,

Mo, van den Bosch; Cooray)

…both separate centrals/satellites

Luminosity dependent clustering

Zehavi et al. 2005 SDSS

• Deviation from power-law statistically significant• Centre plus Poisson satellite model (two free parameters) provides good description

Why is …• Luminosity dependence of SDSS clustering

well described by halo model with

g1(m|L) ≈ 1 + m/[23 m1(L)]

• g1(m|L) nonzero only if m>m1, where m1(L) adjusted to match decrease of number density with increasing L

• (Assume Poisson distribution, with mean g1, for non-central, ‘satellite’ galaxies)

Halo Substructure

• Halo substructure = galaxies is good model (Klypin et al. 1999; Kravtsov et al. 2005)

• Agrees with semi-analytic models and SPH; gas only cools in deep potential wells (Berlind et al. 2004; Zheng et al. 2005; Croton et al. 2006)

• Setting n(>L) = n(>Vcirc) works well for all clustering analyses to date, including z~3 (Conroy et al. 2006)

Halo substructure = galaxies?

• Nsub(>m|M) = (M/1012 h-1Msun)0.1 (M/m)0.9 /90

– Factor 90 (required to have one subhalo) > 23 (required to have one satellite galaxy), suggests that tidal stripping is factor of ~ 4 in mass

– So M/L for centrals (no stripping) larger than for satellites (lots of stripping) of same L (consistent with

lensing analysis of Limousin et al. 2007)

• Also, if stars closer to halo center, M/L different from Mstellar/L

Ongoing debate over ‘orphan’ galaxies … (e.g. Nagai & Kravtsov 2005)

Predicted correlation between

luminosity and mass

Skibba, Sheth, Connolly, Scranton 2006

<Lcen|M> ~ ln(1 + M/Mcrit)

<Lsat|M> ~ independent of M

Prediction based on halo-model interpretation of clustering in SDSS for galaxy samples with various L cuts (Zehavi et al. 2005)

central

satellitetotal

Skibba & Sheth 2007

Berlind et alYang et alHOD prediction

Assumptions (to test)• Halo profiles depend on mass, not

environment• Galaxy properties, so p(Ngal|L,m), and so

g1(m) and g2(m), depend on halo mass, not environment

• All environmental dependence comes from correlation between halo mass and environment:

n(m|) = [1+b(m)n(m)– Mass function ‘top-heavy’ in dense regions

Halo-model of environmental trend• Three types of pairs: both in same halo, in different

halos but same patch, in different patches

• ξ(r|) = ξ1h(r|) + ξ2h-1p(r|) + ξ2h-2p(r|)

Environments in SDSS

• Least dense regions ~ < −0.8 ~ voids

Aside 1:

Poisson cluster models (thermodynamic, Neg. Binomial) quite accurate,

N.B. Counts are in cells centered on particles

• Environment is number of neighbours within 8Mpc

30% densest

30% least dense

• Assume cosmology → halo profiles, halo abundance, halo clustering

• Calibrate g(m) by matching ngal and ξgal(r) of full sample

• Make mock catalog assuming same g(m) for all environments

• Measure clustering in sub-samples defined similarly to SDSS

SDSS

Abbas & Sheth 2007

Mr<−19.5

Highest density

Lowest density

Mass function top heavy in dense regions

z-space

z-space

Aside 2: Stochastic Nonlinear Bias

• Environmental dependence of halo mass function provides accurate framework for describing bias (curvature = ‘nonlinear’; scatter = ‘stochastic’)

• G1(M,V) = ∫dm N(m|M,V) g1(m)

• Environment = neighbours within 8 Mpc

• Clustering stronger in dense regions

• Dependence on density NOT monotonic in less dense regions!

• Same seen in mock catalogs

SDSS

Abbas & Sheth 2007

• Galaxy distribution remembers that, in Gaussian random fields, high peaks and low troughs cluster similarly

Predicts unexpectedly(?) strong clustering of void galaxies

• On large scales void halos indeed MORE strongly clustered than – dark matter – semi-analytic

model of 2dFGRS

dark matter

2dFGRS

Void halos

Colberg & Sheth 2007

• Environment = neighbours within 8 Mpc

• Clustering stronger in dense regions

• Dependence on density NOT monotonic in less dense regions!

• Same seen in mock catalogs

SDSS

Choice of scale not important

Mass function ‘top-heavy’ in dense regions Massive halos have smaller radii (halos have same density whatever their mass)

Gaussian initial conditions? Void galaxies, though low mass, should be strongly clustered

Little room for additional (e.g. assembly bias) environmental effects

Gastrophysics determined by formation history of parent halo

Correlations with environment

• Traditional approach requires separation into ‘cluster’ and ‘field’, ‘dense’ and ‘under-dense’ (Berlind et al. 2006; Yang et al. 2006)

• Non-trivial in redshift-space, given that many environmental trends small, so accurate separation required

Marks in the SDSS

• WW/DD as function of pair separation r– Measure number of pairs separated by r,

weighted by some observable (the ‘mark’)– Divide by number of pairs each weighted by

mean value of ‘mark’

• Observed marks (luminosity, color)

• Derived marks (stellar mass, age, SFR)

Luminosity as mark in SDSS


Large scale signal consistent with halo bias prediction; no large scale environmental trends

Small scale signal suggests centre special; model with gradual threshold (rather than step) is better

centre not special

centre special

Unweighted signal

Luminosity as mark in

SDSS


Close pairs more luminous only in redder bands

Qualitatively consistent with models

Color as mark in SDSS


Close pairs are redder than average

Long-tailed distributions show clearer signal?

MOPED Marks in SDSS

• MOPED evidence for ‘downsizing’ (Heavens et al. 2004)

• Dependence on environment?

• Expect because luminous galaxies populate denser regions

Luminous galaxies

Lower luminositygalaxies

Sheth, Jimenez, Panter, Heavens 2006

• Radius of circle represents total mass in stars formed, in units of average stellar mass formed at same redshift

• Star formation only in less dense regions at low z?


Combination of MOPED marks + mark correlations shows

star formation rates in regions that are dense today was above average at hi-z, below average at low-z

Ultimate goal

• Halo model not just of luminosity, but of entire SED

• First step: luminosity and color– Allows model of stellar mass, star

formation history as function of halo mass, and hence environment

• Color-magnitude relation ~ independent of group properties

• Distribution of galaxies in relation does depend on group properties

Blanton, Berlind, Hogg 2006

Assume split between red and blue depends on luminosity (determine directly from data); mass dependence entirely from correlation between luminosity and halo mass

SATELLITES

CENTRALS

CENTRALS

Assume bimodal colors = centre-

satellite

… rather than centre-satellite or centre-satellite

Model with red satellites works quite well; so can model stellar mass.

Yet to include ‘conformity’; blue central = blue satellites (Weinmann et al. 2006 based on

Yang et al. 2005 group catalog)

Passive evolution of the most

massive galaxies?

White et al. 2007

Match number densities of most luminous galaxies at two redshifts (e.g. NDWFS of Brown et al. 2006)

If no merging …• G(M) = ∫ dm N(m|M) [gcen(m) + gsat(m)]• Low-z bias = ∫ dM n(M) G(M) B(M)• High-z bias = ∫ dm n(m) g(m) b(m)

– Check that two bias factors evolve as expected from linear theory/continuity equation calculation for large scales

• Get small scales by assuming ‘satellites’ trace halo (NFW) profile

…halo model provides complete analytic description

Hi-z Low-z

Can also …

• Assume mergers are of old centrals (tests assumption that dynamical friction primary mechanism for mergers)

• This predicts fraction of ‘merged satellites’ (e.g. White et al. NDWFS ‘satellite’ merger models)

Conclusions• Mark statistics useful for quantifying trends with

environment • Halo model simple, powerful

– useful for understanding environmental trends (halo mass-based description more efficient than density?)

– allows simple description of evolution in no merger models

– first step to building halo-model of SED says satellites are old and red

• Allows one to use abundance and clustering to constrain models (a la Sheth-Tormen for halo mass function)

evolution and environment

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