Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866915008321945600 |
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| author | Berbel, Marina Miravet-Tenés, Miquel Chaudhary, Sushant Sharma Albanesi, Simone Cavaglià, Marco Zertuche, Lorena Magaña Tseneklidou, Dimitra Zheng, Yanyan Coughlin, Michael W. Toivonen, Andrew |
| author_facet | Berbel, Marina Miravet-Tenés, Miquel Chaudhary, Sushant Sharma Albanesi, Simone Cavaglià, Marco Zertuche, Lorena Magaña Tseneklidou, Dimitra Zheng, Yanyan Coughlin, Michael W. Toivonen, Andrew |
| contents | Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-Nearest Neighbors and Random Forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist electromagnetic telescopes in deciding whether to follow up on gravitational-wave events in real time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00045 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations Berbel, Marina Miravet-Tenés, Miquel Chaudhary, Sushant Sharma Albanesi, Simone Cavaglià, Marco Zertuche, Lorena Magaña Tseneklidou, Dimitra Zheng, Yanyan Coughlin, Michael W. Toivonen, Andrew High Energy Astrophysical Phenomena Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-Nearest Neighbors and Random Forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist electromagnetic telescopes in deciding whether to follow up on gravitational-wave events in real time. |
| title | Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2311.00045 |