1. 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)

2. 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: SAM’s, direct gas dynamic • 100 sq deg now, several x 1000 sq deg by mid-2007

3. 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) methods and resources

4. Springel et al 2005 Millennium Simulation (MS) L=500 Mpc/h Ωm=0.25, ΩL=0.75, h=0.73, s8=0.9 1010 particles mp=8.7e8 Msun/h halo/sub-halo catalogs semi-analytic galaxies test red-sequence cluster finding

5. workflow view of galaxy formation star / SMBH formation

6. Croton et al 2006 galaxy samples 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

7. halo occupation of red-sequence galaxies z = 0.41 regular behavior slope slightly steeper than 1 no funny `dark’ clusters

8. 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 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 r–z color redshift

11. 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 < 0.5 (plurality is minority)

12. cluster richness-mass relation red sequence cluster finding recovers well the intrinsic halo occupation clean: fbest ≥ 0.5 blended: fbest < 0.5 halo cluster

13. conditional likelihood of halo mass at fixed richness halos clusters

14. 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(M|Ngal) ?

15. F. Pearce, L. Gazzola (Nottingham) + Virgo Consortium collaborators R. Stanek, B. Nord (Umich) MS w/ gas: halo space density M200 mass function : run 0 open: DM only filled: DM + gas 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 Evrard et al (2002) `prediction’

16. MS w/ gas: scaling relations gas mass fraction thermal SZ gravity only cool+heat 1 gas temperature DM velocity dispersion

17. 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

18. MS galaxies match b+K band LF’s