WPB 1 3 Galaxy clustering Implementation Enzo Branchini

�WP-B 1. 3: Galaxy clustering - Implementation Enzo Branchini (INAF - Brera, Italy) Lado Samushia (University of Portsmouth, UK) �WP-B 2. 3: Galaxy clustering – Validation Carlton Baugh (Durham University, UK) Matteo Viel (INAF – Trieste, Italy)

Galaxy Clustering – Implementation WP deliverables: Software prototype for computing � Correlation function. � Power-spectrum. � Three-point function. � Bispectrum. and cosmology-independent errors WP Status: � 25 members (mostly staff) - 5 nations. � Mailing list created, census of available expertise performed.

Challenges Standard algorithms for computing 2 pt and 3 pt statistics exits. They have been tested and used on previous surveys. Numerical challenges: � 50, 000 galaxies in spectroscopic survey. � 1, 500, 000 galaxies in imaging survey. �Full sky

Challenges Methodology challenges: �Confusion/Purity �Angular dependence of systematics due to slitless spectroscopy �Density dependence of systematics due to slitless spectroscopy

Challenges �Euclid will make possible extremely high precision measurements of clustering statistics. �Level 0 requirements are σ(w 0). �Need to make sure that observational/methodology induced statistical errors are under control so that Euclid data can achieve its statistical promise.

sub-workpackages (Software) Software prototype for ξ(σ, π), + covariance matrix. Software prototype for P(k, μ) + covariance matrix. Software prototype for η+ covariance matrix. Software prototype for B + covariance matrix.

sub-workpackages (methodology + systematics) Methodology Pair/Triplet counting algorithms Window functions Cosmology-independent errors Observational systematics angular systematics (star density, zodiacal light, ) Deep field/Calibration radial systematics (redshift failure, confusion) Instrumental systematics (degradation with time, etc. )

Link to other WPs Development/Qual ity control WP-A 1 Internal data WP-B 1. 1 Management/In ventory WP-A 2 Selection functions. Galaxy clustering – Implementation WPB 1. 3 Galaxy clustering – Validation WPB 2. 3 Documentation /Definition WPA 3 Clusters WPB 1. 4

Interface with other OUs Mock photometric/spectroscopic surveys OUPHZ/MER OUSIR/SPE/MER OU-LE 3/Galaxy clustering implementation OUSIM Preliminary version of photometric survey + calibration Preliminary version of spectroscopic survey + calibration

Interface with GCSWG �Coordinate basis for computing clustering statistics �gauge invariant coordinates z, θ �Generic module for z, θ-> x, y, z �Correlation between cosmology independent and cosmology dependent covariance matrices.

Galaxy Clustering - Validation tasks: �Algorithms for computation redshift-space P(k 1, k 2) �Algorithms for computation of xi(r_p, pi) �Algorithms for computation of covariance matrices �Algorithms for computation of 3 pt function. �Algorithms for likelihood calculation to include cosmology independent terms

Galaxy Clustering - Validation Inputs: �Validation criteria from Galaxy clustering SWG �Algorithms developed and tested by WP-B 1. 3 �Mock catalogues (with masking + selection functions) from OU-SIM plus Cosmological Simulations WG �Preliminary version of Euclid spectroscopic & photometric surveys