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Zheng Zheng
Dept. of Astronomy, Ohio State University
David Weinberg (Advisor, Ohio State)Andreas Berlind (NYU)Josh Frieman (Chicago)Jeremy Tinker (Ohio State)Idit Zehavi (Arizona)SDSS et al.
Collaborators:
Light traces mass?
Light traces mass?
Snapshot @ z~1100Light-Mass relation well
understood
CMB from WMAP
Galaxies from SDSS
Snapshot @ z~0Light-Mass relation not well
understood
Cosmological Modelinitial conditions
energy & matter contents
Galaxy Formation Theorygas dynamics, cooling
star formation, feedback
m 8 n
Dark Halo Population n(M) (r|M) v(r|M)
Halo Occupation DistributionP(N|M) spatial bias within halos velocity bias within halos
Galaxy ClusteringGalaxy-Mass CorrelationsWeinberg
2002
Halo Occupation Distribution (HOD)Halo Occupation Distribution (HOD)
• P(N|M)
Probability distribution of finding N galaxies in a halo of virial mass M
mean occupation <N(M)> + higher moments
• Spatial bias within halos
Difference in the distribution profiles of dark matter and galaxies within halos
• Velocity bias within halos Difference in the velocities of dark matter and galaxies within halos
e.g., Seljak 2000, Scoccimarro et al. 2001, Berlind & Weinberg 2002
Part IPart I
Constraining Galaxy Bias (HOD) Using Constraining Galaxy Bias (HOD) Using SDSS Galaxy Clustering DataSDSS Galaxy Clustering Data
HOD modeling of two-point correlation functions• Departure from a power law
• Luminosity dependence
• Color dependence
Two-point correlation function of galaxies
1-halo term
2-halo term
HOD of sub-halos
Central:
<Ncen>=1, for MMmin
Satellite:
<Nsat>=(M/M1) , for MMmin
Close to Poisson Distribution (~1) Galaxies
Zheng et al. 2004
HOD Parameterization
Kravtsov et al. 2004
Sub-halos
Two-point correlation function: Departures from a power law
Zehavi et al. 2004aSDSS measurements
Two-point correlation function: Departures from a power law
Zehavi et al. 2004a
2-halo term
1-halo term
Divided by the best-fit power law
Dark matter correlation function
The inflection around 2 Mpc/h can be naturally explained within the framework of the HOD:
It marks the transition from a large scale regime dominated by galaxy pairs in separate dark matter halos (2-halo term) to a small scale regime dominated by galaxy pairs in same dark matter halos (1-halo term).
Two-point correlation function: Departures from a power law
Daddi et al. 2003
Strong clustering of a population of red galaxies at z~3
HDF-South
Fit the data by assuming an r-1.8 real space correlation function
r0 ~ 8Mpc/h
host halo mass > 1013 Msun/h
+ galaxy number density ~100 galaxies in each halo
Two-point correlation function: Departures from a power law
Zheng 2004
HOD modeling of the clustering
of z~3 red galaxies
Signals are dominated by 1-halo term
M > Mmin ~ 6×1011Msun/h(not so massive)
<N(M)>=1.4(M/Mmin)0.45
Predicted r0 ~ 5Mpc/h
Less surprising models from HOD modeling
Luminosity dependence of galaxy clustering
Zehavi et al. 2004b
Luminosity dependence of galaxy clustering
Zehavi et al. 2004bDivided by a power law
Luminosity dependence of galaxy clustering
Berlind et al. 2003
Luminosity dependence of the HOD
predicted by galaxy formation models
The HOD and its luminosity dependence inferred from fitting SDSS galaxy correlation functions have a general agreement with galaxy formation model predictions
Luminosity dependence of galaxy clustering
• From 2-point correlation functions (Zehavi et al. 2004b)
• From group multiplicity functions (Berlind et al. 2004)
• From populating Virgo simulations (Wechsler et al. 2004)
Comparison of HODs derivedfrom different methods
Agreement at high mass endSystematics at low mass end
Luminosity dependence of galaxy clustering
HOD parameters as a function of galaxy luminosity
Zehavi et al. 2004b Zheng et al. 2004
SA model
Luminosity dependence of galaxy clustering
Predicting correlation functions for luminosity-bin samples
Zehavi et al. 2004b
Luminosity dependence of galaxy clustering
Zehavi et al. 2004bPredicting the conditional luminosity function (CLF)
Zheng et al. 2004
Conditional luminosity function (CLF) predicted by galaxy formation models
Color dependence of galaxy clustering
Zehavi et al. 2004b
Color dependence of galaxy clustering
Zehavi et al. 2004b Berlind et al. 2003, Zheng et al. 2004
Inferred from SDSS dataPredicted by galaxy formation
model
Color dependence of galaxy clustering
Zehavi et al. 2004b
-20<Mr<-19 -21<Mr<-20
Color dependence of galaxy clustering
Zehavi et al. 2004bRed-blue cross-correlation:Prediction vs Measurement
What we learn:
Red and blue galaxies are nearly well-mixed within halos.
Part IIPart II
Constraining Galaxy Bias (HOD) Constraining Galaxy Bias (HOD) and Cosmology Simultaneously and Cosmology Simultaneously
Using Galaxy Clustering DataUsing Galaxy Clustering Data
A Theoretical Investigation
Why useful ?Why useful ?
• Consistency check• Better constraints on cosmological parameters (e.g., 8, m)• Tensor fluctuation and evolution of dark energy• Non-Gaussianity
Tegmark et al. 2004
CosmologyA
Halo PopulationA
HODA
Galaxy ClusteringGalaxy-Mass Correlations
A
CosmologyB
Halo PopulationB
HODB
Galaxy ClusteringGalaxy-Mass Correlations
B
=
Halo populations from distinct cosmological models
Zheng, Tinker, Weinberg, &
Berlind 2002
Changing m
with 8, n, and Fixed
Halo populations from distinct cosmological models
• Changing m only
Halo mass scale shifts (m)
Same halo clustering at same M/M*
Pairwise velocities at same M/M* m0.6
• Changing m but keeping Cluster-normalization
Similar halo clustering and pairwise velocities at fixed M
Different shapes of halo mass functions
• Changing m and P(k) to preserve the shape of halo MF
Similar halo mass functions
Different halo clustering and halo velocitiesHalo Populations from distinct cosmological models are NOT
degenerate. (Zheng, Tinker, Weinberg, & Berlind 2002)
CosmologyA
Halo PopulationA
HODA
Galaxy ClusteringGalaxy-Mass Correlations
A
CosmologyB
Halo PopulationB
HODB
Galaxy ClusteringGalaxy-Mass Correlations
B
=
HOD parameterization
• P(N|M) Mean occupation <N>M
2nd momentum <N(N-1)>M
[Transition from a narrow distribution to a wide distribution]
• Spatial bias within halos Different concentrations of galaxy distribution and dark
matter distribution (c)
• Velocity bias within halos vg= vvm
Motivated by results from semi-analytic galaxy
formation models and SPH simulations
Observational quantities
• Galaxy overdensity g(r)
• Group multiplicity function ngroup(>N)
• Two-point correlation function of galaxies gg(r)
m0.6/bg
• Pairwise velocity dispersion v(r)
• Average virial mass of galaxy groups <Mvir(N)>
• Galaxy-mass cross-correlation function mgm(r)
• 3-point correlation function of galaxies
Constraints on HOD and cosmological parameters
Changing m
with 8, n, and Fixed
Zheng & Weinberg 2004
Constraints on HOD parameters
Changing m
with 8, n, and Fixed
Constraints on cosmological parameters
Changing m only
Changing 8 only
Cluster-normalized
Halo MF matched
Summary and Conclusion • HOD is a powerful tool to model galaxy clustering.
• HOD modeling aids interpretation of SDSS galaxy clustering.
* HOD leads to informative and physical explanations of galaxy clustering (departures from a power law in 2-point correlation function, the luminosity dependence, and the color dependence) .
* It is useful to separate central and satellite galaxies.
* HODs inferred from the data have a general agreement with those predicted by galaxy formation models.
• Galaxy bias and cosmology are not degenerate w.r.t. galaxy clustering.
* Using galaxy clustering data, we can learn the HOD of different classes of galaxies, and thus provide useful constraints to the theory of galaxy formation.
* Simultaneously, cosmological parameters can also be determined from galaxy clustering data. [future applications to SDSS data]