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

Creators: Stancioli, Rodrigo; Wittman, David; Finner, Kyle; Bouhrik, Faik

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 ± 519 km s⁻¹ line-of-sight velocity difference between them). The X-ray morphology shows two surface brightness peaks between the brightest cluster galaxies (BCGs). We construct weak-lensing mass maps that reveal a mass peak associated with each subcluster. Fitting Navarro–Frenk–White profiles to the lensing data, we find masses of M_200c = 36 ± 11 × 10¹³ and 38 ± 11 × 10¹³M_⊙h⁻¹ for the southern and northern subclusters, respectively. From the mass maps, we infer that the two mass peaks are separated by 520₋₁₂₅⁺¹⁶² 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 and 236 Myr after pericenter, with the merger axis within 28° of the plane of the sky.

Copyright and License

© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Acknowledgement

We thank the anonymous referee for the valuable comments and suggestions. We thank Gastão Lima Neto for guidance in the XMM-Newton data reduction. We thank Nissim Kanekar for help with GMRT exposure time calculations and Huib Intema for help with the SPAM pipeline. We thank Reinout van Weeren and Andrea Botteon for guidance in the GMRT data reduction. This work was supported by NSF grant No. 2308383. We thank the staff of the GMRT that made these observations possible. GMRT is run by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. Some of the data presented herein were obtained at Keck Observatory, which is a private 501(c)3 nonprofit organization operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the Native Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.

Data Availability

Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The specific observations analyzed can be accessed via doi:10.17909/bs78-sw95.

Facilities

Keck:II (Deimos) - , HST (ACS) - , GMRT - Giant Meter-wave Radio Telescope, XMM - Newton X-Ray Multimirror Mission satellite

Software References

SAS (v19.0.0; Gabriel et al. 2004), XSpec (v12.11.1; Arnaud 1996), mc3gmm code (Golovich et al. 2019), FIATMAP code (Wittman et al. 2006), SExtractor (Bertin & Arnouts 1996), GalSim (Rowe et al. 2015), SciPy (Virtanen et al. 2020), Astropy (Astropy Collaboration et al. 2022), Matplotlib (Hunter 2007), NumPy (Harris et al. 2020), Astroquery (Ginsburg et al. 2019), Scikit-learn (Pedregosa et al. 2011)

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