Deep Synoptic Array Science: A Massive Elliptical Host Among Two Galaxy-cluster Fast Radio Bursts

Creators: Sharma, Kritti; Somalwar, Jean; Law, Casey; Ravi, Vikram; Catha, Morgan; Chen, Ge; Connor, Liam; Faber, Jakob T.; Hallinan, Gregg; Harnach, Charlie; Hellbourg, Greg; Hobbs, Rick; Hodge, David; Hodges, Mark; Lamb, James W.; Rasmussen, Paul; Sherman, Myles B.; Shi, Jun; Simard, Dana; Squillace, Reynier; Weinreb, Sander; Woody, David P.; Yadlapalli, Nitika; Deep Synoptic Array team

Abstract

The stellar population environments that are associated with fast radio burst (FRB) sources provide important insights for developing their progenitor theories. We expand the diversity of known FRB host environments by reporting two FRBs in massive galaxy clusters that were discovered by the Deep Synoptic Array (DSA-110) during its commissioning observations. FRB 20220914A has been localized to a star-forming, late-type galaxy at a redshift of 0.1139 with multiple starbursts at lookback times less than ∼3.5 Gyr in the A2310 galaxy cluster. Although the host galaxy of FRB 20220914A is similar to typical FRB hosts, the FRB 20220509G host stands out as a quiescent, early-type galaxy at a redshift of 0.0894 in the A2311 galaxy cluster. The discovery of FRBs in both late- and early-type galaxies adds to the body of evidence that the FRB sources have multiple formation channels. Therefore, even though FRB hosts are typically star-forming, there must exist formation channels that are consistent with old stellar population in galaxies. The varied star formation histories of the two FRB hosts that we report here indicate a wide delay-time distribution of FRB progenitors. Future work in constraining the FRB delay-time distribution, using the methods that we develop herein, will prove crucial in determining the evolutionary histories of FRB sources.

Additional Information

© 2023. 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. The authors thank the staff members of the Owens Valley Radio Observatory and the Caltech radio group, including Kristen Bernasconi, Stephanie Cha-Ramos, Sarah Harnach, Tom Klinefelter, Lori McGraw, Corey Posner, Andres Rizo, Michael Virgin, Scott White, and Thomas Zentmyer. Their tireless efforts were instrumental to the success of the DSA-110. The DSA-110 is supported by the National Science Foundation Mid-Scale Innovations Program in Astronomical Sciences (MSIP) under grant AST-1836018. We acknowledge the use of the VLA calibrator manual and the radio fundamental catalog. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is 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. This research uses services or data provided by the Astro Data Lab at NSFs NOIRLab. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under a cooperative agreement with the National Science Foundation. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has made use of NASAs Astrophysics Data System Bibliographic Services. This research has made use of the VizieR catalog access tool, CDS, Strasbourg, France (DOI:10.26093/cds/vizier). The original description of the VizieR service was published in 2000, A&AS 143, 23. This research made use of Astropy, a community-developed core Python package for Astronomy. Facilities: DSA-110 - , Keck:I (LRIS) (Oke et al. 1995). - Software: astropy (Astropy Collaboration et al. 2013, 2018), NumPy (Harris et al. 2020), SciPy (Virtanen et al. 2020), Matplotlib (Hunter 2007), lpipe (Perley 2019), pPXF (Cappellari 2017, 2022), Prospector (Johnson et al. 2021), emcee (Foreman-Mackey et al. 2013), astropath (Aggarwal et al. 2021), astro-datalab.

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