A new method to assign galaxy cluster membership using photometric redshifts
Gianluca Castignani (Lagrange Laboratory, Nice, CNES) and, Christophe Benoist (Lagrange Laboratory, Nice)

TL;DR
This paper presents a Bayesian method for assigning galaxy cluster membership probabilities using photometric redshifts, considering various galaxy distributions, and validated on simulated data for wide-field surveys.
Contribution
The paper introduces a novel non-parametric Bayesian approach to determine galaxy cluster membership probabilities from photometric redshifts, applicable to large surveys.
Findings
Achieves median purity of 55% and completeness of 95% for galaxies brighter than 0.25L*
Provides unbiased richness estimates with a scatter of 0.10 dex in halo mass
Requires accurate photometric redshifts and robust cluster center estimates
Abstract
We introduce a new effective strategy to assign group and cluster membership probabilities to galaxies using photometric redshift information. Large dynamical ranges both in halo mass and cosmic time are considered. The method takes the magnitude distribution of both cluster and field galaxies as well as the radial distribution of galaxies in clusters into account using a non-parametric formalism and relies on Bayesian inference to take photometric redshift uncertainties into account. We successfully test the method against 1,208 galaxy clusters within redshifts and masses drawn from wide field simulated galaxy mock catalogs developed for the Euclid mission. Median purity and completeness are reached for galaxies brighter than 0.25 within of each simulated halo and for a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
