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Hierarchical inference of the relationship between Concentration and Mass in Galaxy Groups and Clusters

Lieu, M and Farr, WM and Betancourt, M and Smith, GP and Sereno, M and McCarthy, IG (2017) Hierarchical inference of the relationship between Concentration and Mass in Galaxy Groups and Clusters. Monthly Notices of the Royal Astronomical Society, 468 (4). pp. 4872-4886. ISSN 0035-8711

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Abstract

Mass is a fundamental property of galaxy groups and clusters. In theory weak gravitational lensing will enable an approximately unbiased measurement of mass, but parametric methods for extracting cluster masses from data require the additional knowledge of concentration. Measurements of both mass and concentration are limited by the degeneracy between the two parameters, particularly in low mass, high redshift systems where the signal-to-noise is low. In this paper we develop a hierarchical model of mass and concentration for mass inference we test our method on toy data and then apply it to a sample of galaxy groups and poor clusters down to masses of $\sim$ 1e13 M$_\odot$. Our fit and model gives a relationship among masses, concentrations and redshift that allow prediction of these parameters from incomplete and noisy future measurements. Additionally the underlying population can be used to infer an observationally based concentration-mass relation. Our method is equivalent to a quasi- stacking approach with the degree of stacking set by the data. We also demonstrate that mass and concentration derived from pure stacking can be offset from the population mean with differing values depending on the method of stacking.

Item Type: Article
Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Uncontrolled Keywords: 0201 Astronomical And Space Sciences
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Divisions: Astrophysics Research Institute
Publisher: Oxford University Press
Related URLs:
Date Deposited: 26 Jun 2017 10:44
Last Modified: 10 Sep 2017 04:02
DOI or Identification number: 10.1093/mnras/stx686
URI: http://researchonline.ljmu.ac.uk/id/eprint/6747

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