Weak lensing mass bias and the alignment of centre proxies

Author:

Sommer Martin W1ORCID,Schrabback Tim12ORCID,Ragagnin Antonio345ORCID,Rockenfeller Robert6

Affiliation:

1. Argelander-Institut für Astronomie , Auf dem Hügel 71, D-53121 Bonn , Germany

2. Universität Innsbruck, Institut für Astro- und Teilchenphysik , Technikerstr. 25/8, 6020 Innsbruck , Austria

3. Dipartimento di Fisica e Astronomia ‘Augusto Righi’, Alma Mater Studiorum Università di Bologna , via Gobetti 93/2, I-40129 Bologna , Italy

4. INAF – Osservatorio Astronomico di Trieste , via G.B. Tiepolo 11, I-34143 Trieste , Italy

5. IFPU – Institute for Fundamental Physics of the Universe , Via Beirut 2, I-34014 Trieste , Italy

6. Mathematisches Institut, Universität Koblenz , Universtitätsstr. 1, D-56070 Koblenz , Germany

Abstract

ABSTRACT Galaxy cluster masses derived from observations of weak lensing suffer from a number of biases affecting the accuracy of mass-observable relations calibrated from such observations. In particular, the choice of the cluster centre plays a prominent role in biasing inferred masses. In the past, empirical miscentring distributions have been used to address this issue. Using hydrodynamic simulations, we aim to test the accuracy of weak lensing mass bias predictions based on such miscentring distributions by comparing the results to mass biases computed directly using intracluster medium (ICM)-based centres from the same simulation. We construct models for fitting masses to both centred and miscentred Navarro–Frenk–White profiles of reduced shear, and model the resulting distributions of mass bias with normal and lognormal distributions. We find that the standard approach of using miscentring distributions leads to an overestimation of cluster masses at levels of between 2 per cent and 6 per cent when compared to the analysis in which actual simulated ICM centres are used, even when the underlying miscentring distributions match in terms of the miscentring amplitude. While we find that neither lognormal nor normal distributions are generally reliable for accurately modelling the shapes of the mass bias distributions, both models can serve as reasonable approximations in practice.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Oxford University Press (OUP)

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