CoMaLit-V. Mass forecasting with proxies. Method and application to weak lensing calibrated samples

TL;DR

This paper introduces a Bayesian hierarchical method for estimating astronomical object masses using proxies, effectively addressing biases and systematics, and applies it to calibrate weak lensing cluster samples.

Contribution

The paper presents a novel Bayesian hierarchical approach for mass estimation that accounts for biases and does not require representative calibration samples.

Findings

Planck masses are biased low compared to weak lensing masses, especially at high redshift.

MCXC masses are underestimated by about 20%, likely due to hydrostatic bias.

The method successfully calibrates masses for different cluster samples.

Abstract

Mass measurements of astronomical objects are most wanted but still elusive. We need them to trace the formation and evolution of cosmic structure but we can get direct measurements only for a minority. This lack can be circumvented with a proxy and a scaling relation. The twofold goal of estimating the unbiased relation and finding the right proxy value to plug in can be hampered by systematics, selection effects, Eddington/Malmquist biases and time evolution. We present a Bayesian hierarchical method which deals with these issues. Masses to be predicted are treated as missing data in the regression and are estimated together with the scaling parameters. The calibration subsample with measured masses does not need to be representative of the full sample as far as it follows the same scaling relation. We apply the method to forecast weak lensing calibrated masses of the Planck, redMaPPer and MCXC clusters. Planck masses are biased low with respect to weak lensing calibrated masses, with a bias more pronounced for high redshift clusters. MCXC masses are under-estimated by ~20 per cent, which may be ascribed to hydrostatic bias. Packages and catalogs are made available with the paper.