Issue |
A&A
Volume 693, January 2025
|
|
---|---|---|
Article Number | A108 | |
Number of page(s) | 12 | |
Section | Astrophysical processes | |
DOI | https://doi.org/10.1051/0004-6361/202452290 | |
Published online | 07 January 2025 |
Features and prospects for kilonova remnant detection with current and future surveys
1
Astrophysics Research Center of the Open University, The Open University of Israel, Ra’anana, Israel
2
Department of Natural Sciences, The Open University of Israel, PO Box 808 Ra’anana 4353701, Israel
3
Department of Physics, The George Washington University, 725 21st Street NW, Washington, DC 20052, USA
4
Research Center for the Early Universe, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
⋆ Corresponding author; sandeepkumaracharya92@gmail.com
Received:
18
September
2024
Accepted:
16
November
2024
We study the observable spectral and temporal properties of kilonova remnants (KNRs) analytically, and point out quantitative differences with respect to supernova remnants. We provide detection prospects of KNRs in the context of ongoing radio surveys. We find that there is a good chance to expect tens of these objects in future surveys with a flux threshold of ∼0.1 mJy. Kilonova remnants from a postulated population of long-lived supermassive neutron star remnants of neutron star mergers are even more likely to be detected, as they are extremely bright and peak earlier. For an ongoing survey with a threshold of ∼1 mJy, we expect to find tens to hundreds of such objects if they are a significant fraction of the total kilonova (KN) population. Considering that there are no such promising KN candidates in presently ongoing surveys, we constrain the fraction of these extreme KN to be no more than 30 percent of the overall KN population. This constraint depends sensitively on the details of ejecta mass and external density distribution.
Key words: stars: neutron / ISM: jets and outflows / ISM: supernova remnants
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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