Using VPERS to study the AGN galaxy environment

1
Disentangling dynamic and geometric distortions
Federico Marulli Dipartimento di Astronomia,
Università di Bologna

• Marulli, Bianchi, Branchini, Guzzo, Moscardini
  and Angulo
• 2012, arXiv1203.1002
• Bianchi, Guzzo, Branchini, Majerotto, de la
  Torre, Marulli, Moscardini and Angulo
• 2012, arXiv1203.1545

2
Bologna cosmology/clustering group

• Carmelita Carbone
• Victor Vera (PhD)
• Fernanda Petracca (PhD)
• Carlo Giocoli
• Roberto Gilli
• Michele Moresco
• Lauro Moscardini
• Andrea Cimatti
• Federico Marulli

N-body with DE and neutrinos forecasts BAO
with new statistics DE and neutrino constraints
from ?(rp,p) HOD and HAM (Halo Abundance
Matching) AGN clustering P(k) clustering of
galaxy clusters galaxy/AGN evolution RSD
Alcock-Paczynski test clustering of
galaxies/AGN
3
Redshift space distortions
How to constract a 3D map

• Ra, Dec, Redshift ? comoving coordinates
• the real comoving distance is
• the observed galaxy redshift
• zc cosmological redshift due to the Hubble flow
• v component of the galaxy peculiar velocity
  parallel to the line-of-sight

Geometric distortions
Observational distortions
Dynamic distortions
4
Dynamic and geometric distortions
The two-point correlation function
geometric distortions
geometric distortions
no distortions
dynamicgeometric distortions
dynamicgeometric distortions
dynamic distortions
5
Modelling the dynamical distortions
The dispersion model
non-linear model
linear model
model parameters
6
Statistical errors on the growth rate
dß/ß
density
bias
Bianchi et al. 2012
7
Effect of redshift errors on ß and s12
Dynamic distortions dz
Only dynamic distortions
dz ? small sistematic error on ß
Dynamic distortions dz
dß 5 for all dz
8
Effect of geometric distortions
Error on the bias
Error on ß
Spurious scale dependence in b(r)
Error on ?(s)/?(r)
GD ? dß is negligible
9
The Alcock-Paczynski test

• Steps of the method
• Choose a cosmological model to convert redshifts
  into comoving coordinates
• Measure ?
• Model only the dynamical distortions
• Go back to 1. using a different test cosmology

10
next future

• 10 N-body simulations with massive neutrinos
  (L2 Gpc/h)
• (1e6 CPU hours at CINECA)
• for
• all-sky mock galaxy catalogues via HOD and
  box-stacking
• all-sky shear maps via box-stacking and
  ray-tracing
• all-sky CMB weak-lensing maps
• end-to-end simulations for BAO and RSD
  statistics
• reference skies for future galaxy/shear/CMB-lensi
  ng probes
• ISW/Rees-Sciama implementation/analysis
• PI Carmelita Carbone

11
Conclusions

• systematic error on ß of up to 10, for small
  bias objects
• small systematic errors for haloes with more
  than 1e13 Msun
• scaling formula for the relative error on ß as a
  function of survey parameters
• the impact of redshift errors on RSD is similar
  to that of small-scale velocity dispersion
• large redshift errors (sv gt1000km/s) introduce a
  systematic error on ß, that can be accounted for
  by modelling f(v) with a gaussian form
• the impact of GD is negligible on the estimate
  of ß
• GD introduce a spurious scale dependence in the
  bias
• AP test ? joint constraints on ß and OM

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Mock halo catalogues

• BASICC simulation by Raul Angulo
• GADGET-2 code
• 14483 DM particles with mass 5.49e10 Msun/h
• periodic box of 1340 Mpc/h on a side
• ?CDM concordance cosmological framework
• (Om0.25, Ob0.045, O?0.75, h0.73, n1,
  s80.9)
• DM haloes FOF Mgt1e12 Msun/h
• Z1

13
Systematic errors on the growth rate
10
14
Errors on ß on different mass ranges

• Small masses Mlt5e12 Msun/h
• ? systematic error on ß 10
• Intermediate masses 5e12ltMlt2e13 Msun/h
• ? systematic error is small
• ? the linear model works accurately
• Large masses Mgt2e13 Msun/h
• ? large random errors

15
Statistical errors vs Volume
16
Effect of redshift errors on ß and s12
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Effect of geometric distortions
1D correlation function
deprojected correlation
18
Effect of redshift errors on 1D ?