Modeling the dependence of galaxy clustering on stell ar mass and SEDs Lan Wang Collaborators: Guinevere Kauffmann (MP A) Cheng Li (MPA/SHAO, U STC) Gabriella De Lu cia (MPA)
Dec 13, 2015
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)
Outline
• Introduction: theory of galaxy formation
• New parameterized models:Modeling galaxy clustering in a high-resolutio
n simulation of structure formation
Modeling the dependence of clustering on spectra energy distributions of galaxies
Galaxy formation
• Galaxy formation includes two steps:
e.g. White & Rees 1978Dark matter haloes form through gravitational
collapseGalaxies form in dark matter halos by cooling
of baryonic material
— physical processes: gas cooling, star formation, SN feedback, AGN feedback, mergers etc.
Properties of dark matter haloes
Cold dark matter cosmology: structures grow hierarchically
• Dark matter halos:Abundance Press & Schechter 1974Merger treeDensity profile (NFW) Navarro, Frenk & White
1996,1997
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( | )L M dL( | )P N M
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
Minfall - halo mass at infall time tinfall
‘Orphan’ galaxies – satellites without subhalos
vs. HOD: halo mass of today
Two steps
• Minfall →Mstars
• tform, tinfall →SFH
→Dn4000
The Millennium Simulation Springel et al. 2005
8
3 8 1
1
Cosmological parameters:
0.25, 0.045, 0.75
0.73, 1, 0.9
Particles: 2160 , 8.6 10
Boxsize: 500
m b
h n
N h M
h Mpc
• Fit stellar mass function
& clustering for different stellar mass bins
Application to SDSS
Separate relations for central/satellite give better fit
central young & satellite old?
SAM including AGN feedback e.g. Croton et al. 2006; Bower et al. 2006
tform tinfall tpresent
Modelling SFH
Log(
SF
R)
central satellite
• Exponentially evolved SFR with time scales and( )c M ( )s M
• Minfall Mstars metallicity
Gallazzi et al. 2005
• tform, tinfall, SFH
metallicity SFH BC03
Clustering dependence on SEDs
Dn4000 +Mstars
positions
,c s
Non-parametric fit
1/c c • For central galaxies, is parameterized by a sum of Gaussians:
( )s M
• For satellite galaxies, assume simple Gaussian dispersion for
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
SFH: compared with SAM (De Lucia & Blaizot 2006)
• e-folding time scale for satellites
our model: ~2-2.5 Gyr
SAM: ~1Gyr
Evolution to higher redshifts• Dn4000 – local density relation
• VVDS & DEEP2 Cucciati et al. 2006 Cooper et al. 2006
redshifts: 0 0.3 0.8 1.5 2 3
Conclusions
A new statistical model of galaxy clustering• Double power-law form for Mstars ~ Minfall relation• Applied to SDSS: For a given Minfall, satellites are
less luminous and less massive than centrals
Clustering dependence on SEDs reproduced Massive central galaxies have ceased forming st
ars; At low stellar masses, a significant fraction of central galaxies have recent starbursts
Satellite galaxies of all masses have declining SFR, with ~ 2.5s Gyr