A New Dissociative Galaxy Cluster Merger: RM J150822.0+575515.2

Authors: Rodrigo Stancioli, David Wittman, Kyle Finner, Faik Bouhrik

arXiv: 2307.10174v1 - DOI (astro-ph.CO)
License: CC BY 4.0

Abstract: Galaxy cluster mergers that exhibit clear dissociation between their dark matter, intracluster gas, and stellar components are great laboratories for probing dark matter properties. Mergers that are binary and in the plane of the sky have the additional advantage of being simpler to model, allowing for a better understanding of the merger dynamics. We report the discovery of a galaxy cluster merger with all these characteristics and present a multiwavelength analysis of the system, which was found via a search in the redMaPPer optical cluster catalog. We perform a galaxy redshift survey to confirm the two subclusters are at the same redshift (0.541, with $368\pm519$ km s$^{-1}$ line-of-sight velocity difference between them). The X-ray morphology shows two surface-brightness peaks between the BCGs. We construct weak lensing mass maps that reveal a mass peak associated with each subcluster. Fitting NFW profiles to the lensing data, we find masses of $M_{\rm 200c}=36\pm11\times10^{13}$ and $38\pm11\times10^{13}$ M$_\odot/h$ for the southern and northern subclusters respectively. From the mass maps, we infer that the two mass peaks are separated by $520^{+162}_{-125}$ kpc along the merger axis, whereas the two BCGs are separated by 697 kpc. We also present deep GMRT 650 MHz data to search for a radio relic or halo, and find none. Using the observed merger parameters, we find analog systems in cosmological n-body simulations and infer that this system is observed between 96-236 Myr after pericenter, with the merger axis within $28^{\circ}$ of the plane of the sky.

Submitted to arXiv on 19 Jul. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.