A New Method to Quantify X-ray Substructures in Clusters of Galaxies
Felipe Andrade-Santos, Gast\~ao B. Lima Neto, Tatiana F. Lagan\'a

TL;DR
This paper introduces a novel method for quantifying substructures in galaxy clusters using residual X-ray images, calibrates it on Chandra data, and explores its relation to cluster properties, revealing differences in scaling relations based on substructure levels.
Contribution
The paper presents a new residual image analysis technique to quantify galaxy cluster substructures and examines its implications for cosmological scaling relations.
Findings
Method accurately recovers substructure levels in high signal-to-noise clusters.
No correlation found between substructure level and cluster mass, temperature, or redshift.
Different scaling relations for high and low substructure clusters impact cosmological studies.
Abstract
We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model (beta-model or S\'ersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range 0.02<z<0.2 and have a signal-to-noise ratio greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high and low substructure levels. We conclude, using Monte Carlo simulations, that the method…
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.
