Likelihood-free inference for gravitational-wave data analysis and public alerts
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arXiv
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| Format: | Preprint |
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2025
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| author | Marx, Ethan Chatterjee, Deep Desai, Malina Kumar, Ravi Benoit, William Sasli, Argyro Singer, Leo Coughlin, Michael W. Harris, Philip Katsavounidis, Erik |
| author_facet | Marx, Ethan Chatterjee, Deep Desai, Malina Kumar, Ravi Benoit, William Sasli, Argyro Singer, Leo Coughlin, Michael W. Harris, Philip Katsavounidis, Erik |
| contents | Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing the potential of multi-messenger astronomy. Machine learning based detection and parameter estimation algorithms are emerging as production ready alternatives to traditional approaches. Here, we report validation studies of AMPLFI, a likelihood-free inference solution to low-latency parameter estimation of binary black holes. We use simulated signals added into data from the LIGO-Virgo-KAGRA's (LVK's) third observing run (O3) to compare sky localization performance with BAYESTAR, the algorithm currently in production for rapid sky localization of candidates from matched-filter pipelines. We demonstrate sky localization performance, measured by searched area and volume, to be equivalent with BAYESTAR. We show accurate reconstruction of source parameters with uncertainties for use distributing low-latency coarse-grained chirp mass information. In addition, we analyze several candidate events reported by the LVK in the third gravitational-wave transient catalog (GWTC-3) and show consistency with the LVK's analysis. Altogether, we demonstrate AMPLFI's ability to produce data products for low-latency public alerts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Likelihood-free inference for gravitational-wave data analysis and public alerts Marx, Ethan Chatterjee, Deep Desai, Malina Kumar, Ravi Benoit, William Sasli, Argyro Singer, Leo Coughlin, Michael W. Harris, Philip Katsavounidis, Erik General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing the potential of multi-messenger astronomy. Machine learning based detection and parameter estimation algorithms are emerging as production ready alternatives to traditional approaches. Here, we report validation studies of AMPLFI, a likelihood-free inference solution to low-latency parameter estimation of binary black holes. We use simulated signals added into data from the LIGO-Virgo-KAGRA's (LVK's) third observing run (O3) to compare sky localization performance with BAYESTAR, the algorithm currently in production for rapid sky localization of candidates from matched-filter pipelines. We demonstrate sky localization performance, measured by searched area and volume, to be equivalent with BAYESTAR. We show accurate reconstruction of source parameters with uncertainties for use distributing low-latency coarse-grained chirp mass information. In addition, we analyze several candidate events reported by the LVK in the third gravitational-wave transient catalog (GWTC-3) and show consistency with the LVK's analysis. Altogether, we demonstrate AMPLFI's ability to produce data products for low-latency public alerts. |
| title | Likelihood-free inference for gravitational-wave data analysis and public alerts |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2509.22561 |