Transcript Void Statistics - University of Groningen
The Void Probability function and related statistics
Sophie Maurogordato CNRS, Observatoire de la Cote d’Azur, France
The Void probability function
Count probability P N (V): probability of finding N galaxies in a randomly chosen volume of size V N= 0: Void Probability Function P 0 (V) Related to the hierarchy of n-point reduced correlation functions (White 1979) P 0 ( V) exp i 1 (-n) i i!
(
x
1 ,
x
2 ,...,
x i
)
dV
1 ...
dV i
Why the VPF ?
Statistical way to quantify the frequency of voids of a given size.
Complementary information on high-order correlations that low order correlations do not contain: strongly motivated by the existence of large-scale clustering patterns (walls, voids filaments).
Straightforward calculated.
But density dependent, denser samples have smaller voids: be careful when comparing samples with different densities.
Scaling properties for correlation functions Observational evidence for low orders:
n=3 (
r
1 ,
r
2 ,
r
3 )
Q
3 [ 13 23 13 23 ] (Groth & Peebles, 1977, Fry & Peebles 1978, Sharp et al 1984) n=4 4 (
r
1 ,
r
2 ,
r
3 ,
r
4 )
Q
4 ( 12 23 34
perm
.)
Q
4 ( 12 13 14
perm
.) (Fry & Peebles 1978)
Hierarchical models
Generalisation for the reduced N-point correlation N N (
r
1 ,
r
2 ,...,
r N
) a
Q
a N
L
( a N
ij
: a : tree shape L( a ) labellings of a given tree (Fry 1984, Schaeffer 1984, Balian and Schaeffer 1989)
N
1
V N V
d
3
r
1
d
3
r
2 ...
d
3
r N
N (
r
1 ,...,
r N
)
N
(
N
1 )
S N
Scaling invariance expected for the correlation functions of matter
In the linear- and mildly non linear regime : Evolution under predictions for S N gravitational instability of initial gaussian fluctuation; can be followed by perturbation theory >> ’s (Peebles 1980, Jusckiewicz, Bouchet & Colombi 1993, Bernardeau 1994, Bernardeau 2002) S N independant on W, L and z ! In the strongly non-linear regime : solution of the BBGKY equations
Scaling of the VPF under the hierarchical « ansatz »
P
0 exp
N
1
S N
(
nV
)
N N
!
(
N
1 ) The reduced VPF writes:
Log
(
P
0 )
c N c
nV
nV
N
c
)
N
1
S N N
!
The reduced VPF as a function of N c whole set of SN’s
N c
N
1
is a function of the
VPF from galaxy surveys
Zwicky catalog: Sharp 1981 CfA: Maurogordato & Lachièze-Rey 1987 Pisces-Perseus: Fry et al. 1989 CfA2: Vogeley et al. 1991, Vogeley et al. 1994 SSRS: Maurogordato et al.1992, Lachièze-Rey et al. 1992 Huchra’s compilation: Einasto et al. 1991 QDOT: Watson & Rowan-Robinson, 1993 SSRS2: Benoist et al. 1999 2dFGRS: Croton et al. 2004, Hoyle & Vogeley 2004 DEEP2 and SDSS: Conroy et al. 2005 Not exhaustive!
How to compute it ?
Select sub-samples of constant density: volume and magnitude limited samples.
Randomly throw N spheres of volume V and calculate the whole CPDF: P N (V), P 0 (V). N c from the variance of counts.
Volume-averaged correlation functions from the cumulants Test for scale-invariance for the VPF and for the reduced volume-averaged correlation functions.
Scaling or not scaling for the VPF ?
First generation of catalogs: CfA, SSRS, CfA2, SSRS2 First evidences of scaling , but not on all samples. Large scale structures of size comparable to that of the survey Problem of « fair sample » New generation of catalogs: 2dFGRS, SDSS: Excellent convergence to a common function the negative binomial model.
corresponding to
Statistical analysis of the SSRS Reduced VPF’s rescales to the same function even for samples with very different amplitudes of the correlation functions . M>-18, D< 40h -1 Mpc M>-19, D< 60 h -1 Mpc M>-20, D < 80h -1 Mpc From Maurogordato et al. 1992
Void statistics of the CfA redshift Survey
From Vogeley, Geller and Huchra, 1991, ApJ, 382, 44
Scaling of the reduced VPF in the 2DdFGRS From Croton et al., 2004, MNRAS, 352, 828 Enormous range of Nc tested: up to ~40 !
Excellent agreement with the negative binomial distribution Converges towards a universal function at z <0.2
Scaling at high redshift
Different colors Gaussian Thermodynamic Negative binomial Different Luminosities 0.12 < z < 0.5
M>-19.5
M>-20 M>-20.5
M>-21 VPF from DEEP2 (Conroy et al. 2005) VPF from VVDS (Cappi et al. in prep.) Seems to work also at high z !
Real/redshift space distorsions
Small scales: random pairwise velocities Large scales: coherent infall (Kaiser 1997) Distorsion on 2-pt correlation from peculiar velocities in the 2dFGRS From Hawkins et al.,2003
Void statistics in real and redshift space
Vogeley et al. 1994, Little & Weinberg 1994 Voids appear larger in redshift space : Amplification of large-scale fluctuations Model dependant Small scales: VPF is reduced in redshift space due to fingers of God (small effect) Howevever difference is smaller than uncertainties on data (Little & Weinberg 1994, Tinker et al. 2006)
Scaling for p-point averaged correlation functions
Well verified in many samples, for instance: 2D: APM (Gaztanaga 1994, Szapudi et al.1995, Szapudi et Gaztanaga 1998), EDSGC (Szapudi, Meiksin and Nichol 1996) Deep-range (Postman et al. 1998, Szapudi et al. 2000) SDSS (Szapudi et al. 2002, Gaztanaga 2002) 3D: IRAS 1.2 Jy (Bouchet et al. 1993) CFA+SSRS (Gaztanaga et al. 1994) SSRS2 (Benoist et al. 1999) Durham/UKST and Stromlo-APM (Hoyle et al. 2000) 2dFGRS (Croton et al. 2004, Baugh et al. 2004) to p=5!
Skewness and kurtosis (2D) for the Deeprange and SDSS
No clear evolution of S3 and S4 with z Open: Deeprange Filled: SDSS From Szapudi et al. 2002
S N
S
N
’s for 3D catalogs
Gatzanaga et al. 1994 CFA+ SSRS Benoist et al. 1999 SSRS2 Hoyle et al 2000 Stomlo-APM Durham/UKST Baugh et al. 2004 2dFGRS N=3 1.86 ± 0.07
N=4 N=5 4.15 ± 0.6
N=6 1.80 ± 0.2
5.50 ± 3.0
1.8-2.2 ± 0.4
5.0
± 3.8
1.95 ± 0.18
5.50 ± 1.43
17.8 ± 10.5
16.3 ± 50 Good agreement for S3 and S4 in redshift catalogues
Hierarchical correlations for the VVDS
0.5< z < 1.2
S3 ~ 2 On courtesy of Alberto Cappi and the VVDS consortium
Hierarchical Scaling
for VPF in redshift space Valid for samples with different luminosity ranges, redshift ranges, and bias factors for the reduced volume-averaged N-point correlation function S N ’s roughly constant with scale Good agreement for S3 and S4 in different redshift catalogs But different amplitudes from 2D and 3D measurement (damping of clustering in z space, Lahav et al. 1993) Good agreement with evolution of clustering under gravitational instability from initial gaussian fluctuations
The VPF as a tool to discriminate between models of structure formation
Can gravity alone create such large voids as observed in redshift surveys ? What is the dependence of VPF on cosmological parameters ?
What VPF can tell us about the gaussianity/ non gaussianity of initial conditions ?
Can we infer some clue on the biasing scheme necessary to explain them ?
Dependence on model parameters
Einasto et al. 1991, Weinberg and Cole 1992, Little and Weinberg 1994, Vogeley et al. 1994,… For unbiased models: weak dependance on n (VPF when n ) Insensitive to W and L Good discriminant on the gaussianity of initial conditions For biased models: sensitive to biasing prescription VPF is higher for higher bias factor
What can we learn from VPF (and SN’s) about « biasing » ?
In the « biased galaxy formation » frame, galaxies are expected to form at the high density peaks of the matter density field (Kaiser 1984, Bond et al. 1986, Mo and White 1996,..) Observations show multiple evidences of bias: luminosity, color, morphological bias Variation of the amplitude of the auto-correlation function (Benoist et al. 1996, Guzzo et al. 2000, Norberg et al 2001, Zehavi et al. 2004, Croton et al. 2004)
Luminosity bias from galaxy redshift surveys
From Norberg et al 2001
Testing the bias model with S
N
’s
Linear bias hypothesis:
b
b
2 N
b N
N S N
1
b N
2
S
N
Inconsistency between the the measured values of SN’s towards the expected values from the correlation functions under the linear bias hypothesis (Benoist et al. 1999, Croton et al. 2004)
High order statistics in the SSRS2 S3 should be lower for more luminous (more biased) samples, which is not the case !
From Benoist et al. 1999
Non-linear local bias and high-order moments
g
k
0
b k k
!
k
Fry and Gatzanaga 1993 This local biasing transformation preserves the hierarchical structure in the regime of small Presence of secondary order terms in S N ’s:
S
3
g
1
b
1 (
S
3 3
c
2 )
S
4
g
1
b
1 2 (
S
4 12
c
2
S
3 4
c
3 12
c
2 2 ) Gatzanaga et al 1994, 1995 Benoist et al. 1999 Hoyle et al. 2000 Croton et al. 2004
Constraining the biasing scheme
Galaxy distribution results from gravitational evolution of dark matter coupled to astrophysical processes : gas cooling, star formation, feedback from supernovae… Large-scales: bias is expected to be linear Small scales : bias reflects the physics of galaxy formation, so can be scale-dependant Recent progress in modelling the non-linear clustering: HOD >> bias at the level of dark matter halos (Benson et al. 2001, Berlind & Weinberg 2002, Kravtsov et al. 2004, Conroy et al. 2005, Tinker, Weinberg & Warren 2006)
Constraining the HOD parameters
Berlind and Weinberg 2002, Tinker, Weinberg & Warren 2006 Void statistics expected to be sensitive to HOD at low halo masses BW02:
fmin=2 fmin=4 fmin= ∞ c=-0.2
c 0.4
c 0.6
c 0.8
< c , M min = f min x M min From Tinker, Weinberg, Warren 2006
Conclusions
Convergence of observational results from existing redshift surveys: scale-invariance of the reduced VPF Hierarchical behaviour of N-point averaged correlation functions More: the shape for the reduced VPF, and the amplitudes of S 3 and S 4 are consistent for the different samples.
Good agreement with the gravitational instability model.
VPF in recent surveys + state of the art HOD very promising to constrain the non linear bias
Testing a prescription for bias ?
L CDM + semi-analytic model (Benson et al 2002) Galaxy distribution show more large voids than dark matter.
Matching the VPF >> constrain the feedback mechanisms Benson et al. 2003
The effect of introducing biasing on VPF
• Strongly discriminant Gaussian/non Gaussian if non biasing • Biasing creates large voids in all models • Non gaussinaity is not required to explain current observations Weinberg and Cole 1992
Little & Weinberg 1994
Models for the VPF
BBGKY (Fry 1984) Thermodynamical model (Saslaw & Hamilton 1984) Binomial model (Carruthers & Shih 1983) Log-normal model (Coles & Jones 1991)
What « empty » regions can tell us about « filled » ones ?
How both are connected ?