Inferring Properties of Rotating Core-Collapse Supernovae with Gravitational-Wave Detector Networks
Abstract
Core-collapse supernovae are among the most promising yet still undetected sources of gravitational waves. A future detection would provide a direct view of the physical processes occurring deep inside a collapsing star. In this work, we investigate how networks of current and future gravitational-wave detectors can constrain the properties of rapidly rotating core-collapse supernovae using their characteristic core-bounce and early post-bounce signals. Using deep-learning techniques, we estimate the peak frequency, rotation rate, and signal amplitude from noisy detector data and compare the performance of different detector-network configurations. We find that detector networks improve both parameter recovery and sky coverage. For current-generation networks, estimation of the peak frequency is possible out to about 15 kpc, while the rotation rate remains recoverable out to distances exceeding 100 kpc. Third-generation observatories extend these distances by approximately a factor of three to four.
Versions
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BibTeX
@misc{akhmetali2026inferring,
title = {{Inferring Properties of Rotating Core-Collapse Supernovae with Gravitational-Wave Detector Networks}},
author = {Almat Akhmetali and Y. Sultan Abylkairov and Solange Nunes and José Antonio Font and Michele Zanolin and Ernazar Abdikamalov},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2608.01634},
eprint = {2608.01634},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.01634},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Inferring Properties of Rotating Core-Collapse Supernovae with Gravitational-Wave Detector Networks
AU - Almat Akhmetali
AU - Y. Sultan Abylkairov
AU - Solange Nunes
AU - José Antonio Font
AU - Michele Zanolin
AU - Ernazar Abdikamalov
PY - 2026
DA - 2026/11/04
PB - arXiv
DO - 10.48550/arxiv.2608.01634
UR - https://arxiv.org/abs/2608.01634
AB - Core-collapse supernovae are among the most promising yet still undetected sources of gravitational waves. A future detection would provide a direct view of the physical processes occurring deep inside a collapsing star. In this work, we investigate how networks of current and future gravitational-wave detectors can constrain the properties of rapidly rotating core-collapse supernovae using their characteristic core-bounce and early post-bounce signals. Using deep-learning techniques, we estimate the peak frequency, rotation rate, and signal amplitude from noisy detector data and compare the performance of different detector-network configurations. We find that detector networks improve both parameter recovery and sky coverage. For current-generation networks, estimation of the peak frequency is possible out to about 15 kpc, while the rotation rate remains recoverable out to distances exceeding 100 kpc. Third-generation observatories extend these distances by approximately a factor of three to four.
ER -