The miniJPAS survey

Abstract

Context. Samples of galaxy clusters allow us to better understand the physics at play in galaxy formation and to constrain cosmological models once the mass, position (for clustering studies), and redshift are known. In this context, large optical datasets play a crucial role. Aims. We investigate the capabilities of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) in detecting and characterizing galaxy groups and clusters. We analysed the data of the miniJPAS survey, obtained with the JPAS-Pathfinder camera and covering 1 deg2 centred on the AEGIS field to the same depths and with the same 54 narrow-band filters plus two broader-band near-UV and near-IR filters anticipated for the full J-PAS survey. Methods. We used the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm to detect and characterize groups and clusters of galaxies down to S/N = 2.5 in the redshift range 0.05 < z < 0.8. Results. We detected 80, 30, and 11 systems with signal-to-noise ratios higher than 2.5, 3.0, and 3.5, respectively, down to ∼1013 M⊙ h−1. We derive mass-proxy scaling relations based on Chandra and XMM-Newton X-ray data for the signal amplitude returned by AMICO, the intrinsic richness, and a new proxy that incorporates the galaxies’ stellar masses. This proxy is made possible thanks to the J-PAS filters and shows a smaller scatter with respect to the richness. We fully characterize the sample and use AMICO to derive a probabilistic membership association of galaxies with the detected groups that we test against spectroscopy. We further show how the narrow-band filters of J-PAS provide a gain of up to 100% in signal-to-noise ratio in detection and an uncertainty on the redshift of clusters of only σz = 0.0037(1 + z), placing J-PAS between broad-band photometric and spectroscopic surveys. Conclusions. The performance of AMICO and J-PAS with respect to mass sensitivity, mass-proxies quality, and redshift accuracy will allow us to derive cosmological constraints not only based on cluster counts, but also based on clustering of galaxy clusters.