Measuring the hydrostatic mass bias in galaxy clusters by combining Sunyaev-Zel'dovich and CMB lensing data

TL;DR

This paper combines Sunyaev-Zel'dovich and CMB lensing data to accurately measure the hydrostatic mass bias in galaxy clusters, addressing discrepancies in cosmological models and cluster observations.

Contribution

It provides new constraints on the mass-pressure relation using independent data, reducing tension in cosmological parameters and highlighting potential gaps in simulations.

Findings

Estimated hydrostatic mass bias of 0.26 ± 0.07

No significant mass or redshift evolution of bias

Reduces tension between ΛCDM predictions and tSZ observations

Abstract

The cosmological parameters prefered by the cosmic microwave background (CMB) primary anisotropies predict many more galaxy clusters than those that have been detected via the thermal Sunyaev-Zeldovich (tSZ) effect. This tension has attracted considerable attention since it could be evidence of physics beyond the simplest $\Lambda$CDM model. However, an accurate and robust calibration of the mass-observable relation for clusters is necessary for the comparison, which has been proven difficult to obtain so far. Here, we present new contraints on the mass-pressure relation by combining tSZ and CMB lensing measurements about optically-selected clusters. Consequently, our galaxy cluster sample is independent from the data employed to derive cosmological constrains. We estimate an average hydrostatic mass bias of $b = 0.26 \pm 0.07$, with no significant mass nor redshift evolution. This value greatly reduces the tension between the predictions of $\Lambda$CDM and the observed abundance of tSZ clusters while being in agreement with recent estimations from tSZ clustering. On the other hand, our value for $b$ is higher than the predictions from hydro-dynamical simulations. This suggests the existence of mechanisms driving large departures from hydrostatic equilibrium and that are not included in state-of-the-art simulations, and/or unaccounted systematic errors such as biases in the cluster catalogue due to the optical selection.