Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
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arXiv
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| Format: | Preprint |
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2026
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| author | Akhmetali, Almat Abylkairov, Y. Sultan Orel, Daniil Nunes, Solange Sakan, Aknur Zhunuskanov, Alisher Zaidyn, Marat Ussipov, Nurzhan Font, José Antonio Abdikamalov, Ernazar |
| author_facet | Akhmetali, Almat Abylkairov, Y. Sultan Orel, Daniil Nunes, Solange Sakan, Aknur Zhunuskanov, Alisher Zaidyn, Marat Ussipov, Nurzhan Font, José Antonio Abdikamalov, Ernazar |
| contents | Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova central engine. In this work, we investigate parameter estimation from the bounce and early ring-down GW signal of rotating CCSNe using machine learning. We infer the peak frequency and peak amplitude of the signal as well as the rotation of the core. We extend previous studies in several directions. We consider a range of progenitor models and nuclear equations of state, and we assess the impact of key physical uncertainties, including bounce-time uncertainty and source inclination. We incorporate both current detector noise and the projected sensitivities of next-generation observatories. We find that uncertainty in the bounce time does not significantly affect parameter estimation when the analysis is performed in the Fourier domain. In contrast, orientations when the rotation axis is near the line of sight substantially degrade performance. For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04896 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors Akhmetali, Almat Abylkairov, Y. Sultan Orel, Daniil Nunes, Solange Sakan, Aknur Zhunuskanov, Alisher Zaidyn, Marat Ussipov, Nurzhan Font, José Antonio Abdikamalov, Ernazar High Energy Astrophysical Phenomena Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova central engine. In this work, we investigate parameter estimation from the bounce and early ring-down GW signal of rotating CCSNe using machine learning. We infer the peak frequency and peak amplitude of the signal as well as the rotation of the core. We extend previous studies in several directions. We consider a range of progenitor models and nuclear equations of state, and we assess the impact of key physical uncertainties, including bounce-time uncertainty and source inclination. We incorporate both current detector noise and the projected sensitivities of next-generation observatories. We find that uncertainty in the bounce time does not significantly affect parameter estimation when the analysis is performed in the Fourier domain. In contrast, orientations when the rotation axis is near the line of sight substantially degrade performance. For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc. |
| title | Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2605.04896 |