1. Disentangling dynamic and geometric distortions Federico Marulli Dipartimento di Astronomia, Università di Bologna Marulli, Bianchi, Branchini, Guzzo, Moscardini and Angulo 2012, arXiv:1203.1002 Bianchi, Guzzo, Branchini, Majerotto, de la Torre, Marulli, Moscardini and Angulo 2012, arXiv:1203.1545

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 ξ (r p, π ) 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 Ra, Dec, Redshift  comoving coordinates the real comoving distance is: the observed galaxy redshift: z c : cosmological redshift due to the Hubble flow v || : component of the galaxy peculiar velocity parallel to the line-of-sight How to constract a 3D map Geometric distortions Observational distortions Dynamic distortions

4. Dynamic and geometric distortions The two-point correlation function geometric distortions no distortions dynamic distortions dynamic+geometric distortions geometric distortions dynamic+geometric distortions

5. Modelling the dynamical distortions linear model non-linear model model parameters The “dispersion” model

6. Statistical errors on the growth rate bias density δβ/β Bianchi et al. 2012

7. Effect of redshift errors on β and σ 12 Only dynamic distortions δz  small sistematic error on β δβ ~ 5% for all δz Dynamic distortions + δz

8. Effect of geometric distortions Error on β Error on the bias Error on ξ(s)/ξ(r) GD  δβ is negligible Spurious scale dependence in b(r)

9. The Alcock-Paczynski test Steps of the method 1.Choose a cosmological model to convert redshifts into comoving coordinates 2.Measure ξ only 3.Model only the dynamical distortions 4.Go back to 1. using a different test cosmology

10. …next future 10 N-body simulations with massive neutrinos (L=2 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-lensing probes  ISW/Rees-Sciama implementation/analysis PI Carmelita Carbone

11. Conclusions systematic error on β 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 β 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 (σ v >1000km/s) introduce a systematic error on β, that can be accounted for by modelling f(v) with a gaussian form the impact of GD is negligible on the estimate of β GD introduce a spurious scale dependence in the bias AP test  joint constraints on β and Ω M

12. BASICC simulation by Raul Angulo GADGET-2 code ~1448^3 DM particles with mass 5.49e10 Msun/h periodic box of 1340 Mpc/h on a side Λ CDM “concordance” cosmological framework (Ω m =0.25, Ω b =0.045, Ω Λ =0.75, h=0.73, n=1, σ 8 =0.9) DM haloes: FOF M>1e12 Msun/h Z=1 Mock halo catalogues

13. Systematic errors on the growth rate

14. Errors on β on different mass ranges Small masses [M<5e12 Msun/h]  systematic error on β ~ 10% Intermediate masses [5e12<M<2e13 Msun/h]  systematic error is small  the linear model works accurately Large masses [M>2e13 Msun/h]  large random errors

15. Statistical errors vs Volume

16. Effect of redshift errors on β and σ 12

17. Effect of geometric distortions 1D correlation function deprojected correlation

18. Effect of redshift errors on 1D ξ