Largescale Structure Simulations

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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)
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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
  evolution
• empirical halo occupation, ADDGALS
• first principle SAMs, direct gas dynamic
• 100 sq deg now, several x 1000 sq deg by mid-2007

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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 _at_ BCN (104 cpus,106 hours 100 Tb)
• NCSA allocations of cycles and storage
• local compute clusters (100 cpus) and storage
  (10 Tb)
• each billion particle run generates 10Tb of
  output
• NASA AISR proposal to grid-enable this work
  (follow DM lead)

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Millennium Simulation (MS)
Springel et al 2005
L500 Mpc/h ?m0.25, ?L0.75, h0.73, s80.9
1010 particles mp8.7e8 Msun/h
halo/sub-halo catalogs
semi-analytic galaxies
test red-sequence cluster finding
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workflow view of galaxy formation
star / SMBH formation
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galaxy samples
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 rz
color grows with redshift factor 2 wider than
observed
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halo occupation of red-sequence galaxies
z 0.41
regular behavior slope slightly steeper than 1
no funny dark clusters
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apply simple cluster finder to volume projections

• Aim lower-bound on blending due to supercluster
  projections
• use periodic BCs to re-center volume around
  each galaxy
• - apply linear color gradient to fore/background

• 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 NgalNmin (4)
• repeat for next available (non-assigned) galaxy

rz color
redshift
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cluster classification based on halo matching
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 lt 0.5 (plurality is minority)
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cluster richness-mass relation
red sequence cluster finding recovers well the
intrinsic halo occupation clean fbest
0.5 blended fbest lt 0.5
halo
cluster
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conditional likelihood of halo mass at fixed
richness
halos
clusters
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conditional likelihood of halo mass at fixed
richness
halos
clean clusters
blended clusters
Next step test whether SZ signatures will remove
blends consider bi-modal likelihood
p(MNgal) ?
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MS w/ gas halo space density
F. Pearce, L. Gazzola (Nottingham) Virgo
Consortium collaborators R. Stanek, B. Nord
(Umich)
M200 mass function run 0 open DM only filled
DM gas
5x108 particles mdm1.4x1010 Msun/h mgas2.9x109
Msun/h
3 simulations 0. gravity only 1. cooling
heating I 2. cooling heating II
Evrard et al (2002) prediction
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MS w/ gas scaling relations
gas mass fraction
thermal SZ
gravity only coolheat 1
gas temperature
DM velocity dispersion
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MS w/ gas covariance of observables
high Lx systems are likely to be gas rich
correl. coeff. r 0.5
deviation in gas mass fraction
deviation in X-ray luminosity
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MS galaxies match bK band LFs