1. Modeling the dependence of galaxy clustering on stellar mass and SEDs Lan Wang Collaborators: Guinevere Kauffmann (MPA) Cheng Li (MPA/SHAO, USTC) Gabriella De Lucia (MPA)

2. Outline Introduction ： theory of galaxy formation New parameterized models:  Modeling galaxy clustering in a high- resolution simulation of structure formation  Modeling the dependence of clustering on spectra energy distributions of galaxies

3. Galaxy formation Galaxy formation includes two steps: e.g. White & Rees 1978  Dark matter haloes form through gravitational collapse  Galaxies form in dark matter halos by cooling of baryonic material — physical processes ： gas cooling, star formation, SN feedback, AGN feedback, mergers etc.

4. Properties of dark matter haloes Cold dark matter cosmology: structures grow hierarchically Dark matter halos ：  Abundance Press & Schechter 1974  Merger tree  Density profile (NFW) Navarro, Frenk & White 1996,1997

5. Link galaxy properties to DM halos Hydrodynamic Simulation e.g. White, Hernquist & Springel 2001 Semi-analytic models Kauffmann et al. 1999 Halo Occupation Distribution models (HOD) Berlind & Weinberg 2002 Yang, Mo & van den Bosch 2003

6. SAM HOD

7. Our Methodology Falls in between semi-analytic method & HOD approach:  Positions, velocities and formation history from simulation  Parameterized functions to determine galaxy properties Based on Millennium Simulation

8. M infall - halo mass at infall time t infall ‘Orphan’ galaxies – satellites without subhalos vs. HOD: halo mass of today Two steps M infall →M stars t form, t infall →SFH →D n 4000

9. The Millennium Simulation Springel et al. 2005

11. M stars vs. M infall in semi-analytic catalogue Croton et al. 2006

12. Statistics reproduced vs. semi-analytic results Stellar mass Function Correlation

13. Central, satellite & ‘orphan’ L & M infall Luminosity Function

14. ‘orphan’ galaxies Critical for correlation at small scales

15. Fit stellar mass function & clustering for different stellar mass bins Application to SDSS Separate relations for central/satellite give better fit

16. Fitted relations Mandelbaum et al. 2006 Satellites are less massive than centrals

17. SDSS observation

18. central young & satellite old? SAM including AGN feedback e.g. Croton et al. 2006; Bower et al. 2006

19. t form t infall t present Modelling SFH Log(SFR) central satellite Exponentially evolved SFR with time scales and

20. Bruzual & Charlot 2003 model Concentrate on D n 4000 because of its weak dependence on dust

21. M infall M stars metallicity Gallazzi et al. 2005 t form, t infall, SFH  metallicity SFH BC03 Clustering dependence on SEDs D n 4000 + M stars positions

22. Non-parametric fit For central galaxies, is parameterized by a sum of Gaussians: For satellite galaxies, assume simple Gaussian dispersion for

23. Best-fit: Constant SFR: 0

24. Main results Massive centrals have ceased forming stars At low stellar masses, central galaxies display a wide range of different SFH, with a significant fraction experiencing recent star bursts. Time scale for satellite galaxies is almost independent of stellar mass

25. Consistency checks Specific SFR: our model vs. SDSS results g-r distributions

26. SFH: compared with SAM (De Lucia & Blaizot 2006) e-folding time scale for satellites our model: ~2-2.5 Gyr SAM: ~1Gyr

27. Evolution to higher redshifts D n 4000 – local density relation VVDS & DEEP2 Cucciati et al. 2006 Cooper et al. 2006 redshifts: 0 0.3 0.8 1.5 2 3

28. Conclusions A new statistical model of galaxy clustering Double power-law form for M stars ~ M infall relation Applied to SDSS: For a given M infall, satellites are less luminous and less massive than centrals  Clustering dependence on SEDs reproduced  Massive central galaxies have ceased forming stars; At low stellar masses, a significant fraction of central galaxies have recent starbursts  Satellite galaxies of all masses have declining SFR, with

29. Thank You ！

30. Full non-parametric test Time scale for satellite galaxies is almost independent of stellar mass