Cosmology and astrophysics from relaxed galaxy clusters - IV: Robustly calibrating hydrostatic masses with weak lensing
D. E. Applegate, A. Mantz, S. W. Allen, A. von der Linden, R. G., Morris, S. Hilbert, P. L. Kelly, D. L. Burke, H. Ebeling, D. A. Rapetti, R., W. Schmidt

TL;DR
This study calibrates X-ray hydrostatic masses of relaxed galaxy clusters using weak lensing data, finding good agreement and minimal bias, which enhances the reliability of cluster-based cosmological measurements.
Contribution
It provides a robust calibration of hydrostatic mass estimates with weak lensing, reducing systematic uncertainties in galaxy cluster mass measurements for cosmology.
Findings
Lensing to X-ray mass ratio of 0.96 +/- 9% (stat) +/- 9% (sys)
No significant dependence of mass ratio on mass, redshift, or morphology
Results disfavor large deviations from hydrostatic equilibrium in relaxed clusters
Abstract
This is the fourth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Here, we use measurements of weak gravitational lensing from the Weighing the Giants project to calibrate Chandra X-ray measurements of total mass that rely on the assumption of hydrostatic equilibrium. This comparison of X-ray and lensing masses provides a measurement of the combined bias of X-ray hydrostatic masses due to both astrophysical and instrumental sources. Assuming a fixed cosmology, and within a characteristic radius (r_2500) determined from the X-ray data, we measure a lensing to X-ray mass ratio of 0.96 +/- 9% (stat) +/- 9% (sys). We find no significant trends of this ratio with mass, redshift or the morphological indicators used to select the sample. In accordance with predictions from hydro simulations for the most massive, relaxed clusters,…
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.
