A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908381447454720 |
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| author | Marx, Ethan Benoit, William Blodgett, Trevor Chatterjee, Deep de Bruin, Emma Henderson, Steven Kompanets, Katrine Soni, Siddharth Coughlin, Michael Harris, Philip Katsavounidis, Erik |
| author_facet | Marx, Ethan Benoit, William Blodgett, Trevor Chatterjee, Deep de Bruin, Emma Henderson, Steven Kompanets, Katrine Soni, Siddharth Coughlin, Michael Harris, Philip Katsavounidis, Erik |
| contents | We conduct a search for stellar-mass binary black hole mergers in gravitational-wave data collected by the LIGO detectors during the LIGO-Virgo-KAGRA (LVK) third observing run (O3). Our search uses a machine learning (ML) based method, Aframe, an alternative to traditional matched filtering search techniques. The O3 observing run has been analyzed by the LVK collaboration, producing GWTC-3, the most recent catalog installment which has been made publicly available in 2021. Various groups outside the LVK have re-analyzed O3 data using both traditional and ML-based approaches. Here, we identify 38 candidates with probability of astrophysical origin ($p_\mathrm{astro}$) greater than 0.5, which were previously reported in GWTC-3. This is comparable to the number of candidates reported by individual matched-filter searches. In addition, we compare Aframe candidates with catalogs from research groups outside of the LVK, identifying three candidates with $p_\mathrm{astro} > 0.5$. No previously un-reported candidates are identified by Aframe. This work demonstrates that Aframe, and ML based searches more generally, are useful companions to matched filtering pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run Marx, Ethan Benoit, William Blodgett, Trevor Chatterjee, Deep de Bruin, Emma Henderson, Steven Kompanets, Katrine Soni, Siddharth Coughlin, Michael Harris, Philip Katsavounidis, Erik Instrumentation and Methods for Astrophysics We conduct a search for stellar-mass binary black hole mergers in gravitational-wave data collected by the LIGO detectors during the LIGO-Virgo-KAGRA (LVK) third observing run (O3). Our search uses a machine learning (ML) based method, Aframe, an alternative to traditional matched filtering search techniques. The O3 observing run has been analyzed by the LVK collaboration, producing GWTC-3, the most recent catalog installment which has been made publicly available in 2021. Various groups outside the LVK have re-analyzed O3 data using both traditional and ML-based approaches. Here, we identify 38 candidates with probability of astrophysical origin ($p_\mathrm{astro}$) greater than 0.5, which were previously reported in GWTC-3. This is comparable to the number of candidates reported by individual matched-filter searches. In addition, we compare Aframe candidates with catalogs from research groups outside of the LVK, identifying three candidates with $p_\mathrm{astro} > 0.5$. No previously un-reported candidates are identified by Aframe. This work demonstrates that Aframe, and ML based searches more generally, are useful companions to matched filtering pipelines. |
| title | A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2505.21261 |