Large-scale Structure Simulations

A.E. Evrard, R Stanek, B Nord (Michigan) E. Gaztanaga, P Fosalba, M. Manera (Barcelona)

A. Kravtsov (Chicago)P.M Ricker (UIUC/NCSA)R. Wechsler (Stanford)

D. Weinberg (OSU)

core science areas

• non-linear evolution of the matter density

P(k) for weak lensing, BAO

halo characterization for clusters, BAO, weak lensing

• gas dynamic simulations of clusters

g(ySZ , Ngal, … | Mhalo,z) : form of observable-mass relation

sensitivity to galaxy/AGN physics

• mock sky surveys of galaxies and clusters

SZ + optical cluster finding : test self-calibration

multiple techniques to model galaxy formation and

evolutionempirical: halo occupation, ADDGALS

first principle: SAM’s, direct gas dynamic

100 sq deg now, several x 1000 sq deg by mid-2007

methods and resources

• mpi-based large-scale structure codes

GADGET: tree-PM N-body + Lagrangian hydro (SPH)

ART: tree N-body + Eulerian, adaptive-grid hydro

FLASH: PM N-body + Eulerian, adaptive-grid hydro

• compute resources

Marenostrum @ BCN (104 cpus,106 hours + 100 Tb)

NCSA allocations of cycles and storage

local compute clusters (~100 cpu’s) and storage (~10 Tb)

each billion particle run generates ~10Tb of output

NASA AISR proposal to grid-enable this work (follow DM

lead)

Millennium Simulation (MS)

L=500 Mpc/h

Ωm=0.25, Ω=0.75,

h=0.73, 8=0.9

1010 particles

mp=8.7e8 Msun/h

halo/sub-halo catalogs

semi-analytic galaxies

Springel et al 2005

test red-sequence cluster finding

workflow view of galaxy formation

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star / SMBH formation

galaxy samples

redshift z-mag Number

0.99 22 1054711

0.69 21 1005469

0.41 19.6 942313

Croton et al 2006

2 galaxy types in a halo: central - accrete gas + form stars

satellite - no gas accretion or star formation

red sequence in halos w/ Ngal ≥ 4:

width of r–z color grows with redshift

factor ~2 wider than observed

halo occupation of red-sequence galaxies

z = 0.41

regular behaviorslope slightly steeper

than 1

no funny `dark’ clusters

simple cluster finder based on mean sky density

(parallels 3D algorithm used to define halos) for brightest galaxy

– re-center volume on galaxy

– apply line-of-sight color gradient for z-evolution

– grow disc until mean RS number density threshold is reached

– assign group members if Ngal≥Nmin (=4)

repeat for next available (non-assigned) galaxy

apply simple cluster finder to volume projections

Aim: lower-bound on blending due to supercluster projections

–use periodic BC’s to re-center volume around each galaxy

-- apply linear color gradient to fore/background

r–z

colo

rredshift

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cluster classification based on halo matching

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fbest = Ngal(halo) / Ngal(cluster)

for the halo contributing the

largest number of galaxies

2 classes:

clean : fbest ≥ 0.5

(plurality is majority)

blended : fbest < 0.5

(plurality is minority)

cluster richness-mass relation

red sequence cluster

finding recovers well

the intrinsic halo

occupation

clean : fbest ≥ 0.5

blended : fbest < 0.5

halo

cluster

conditional likelihood of halo mass at fixed richness

clusters

halos

conditional likelihood of halo mass at fixed richness

clean clusters

halos

blended clusters

Next step: test whether SZ signatures will remove blends

consider bi-modal likelihood p(M|Ngal) ?

MS w/ gas: halo space density

5x108 particles

mdm=1.4x1010 Msun/h

mgas=2.9x109 Msun/h

3 simulations: 0. gravity only 1. cooling + heating I 2. cooling + heating II

F. Pearce, L. Gazzola (Nottingham)

+ Virgo Consortium collaborators

R. Stanek, B. Nord (Umich)

M200 mass function : run 0

open: DM only

filled: DM + gas

Evrard et al (2002)

`prediction’

gravity only

cool+heat 1

thermal SZ gas mass fraction

DM velocity dispersion

gas temperature

MS w/ gas: scaling relations

MS w/ gas: covariance of observables

high Lx systems are likely to be gas rich

correl. coeff.

r = 0.5

deviation in X-ray luminosity

devia

tion in g

as

mass

fra

ctio

n

MS galaxies match b+K band LF’s

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