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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.17377 |
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| _version_ | 1866916020666499072 |
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| author | Saha, Surojit Kong, Albert K. H |
| author_facet | Saha, Surojit Kong, Albert K. H |
| contents | The coalescence of binary neutron stars in the GW170817 event led to the generation of gravitational waves, accompanied by the electromagnetic counterpart known as a kilonova (KN). Since then, it has been a prime topic of interest, as it has provided much insight into multi-messenger astronomy. Apart from existing methods for parameter estimation, we propose an alternative technique for it, utilizing the strength and flexibility of a conditional variational autoencoder. Publicly available light curves are used as training data, conditioning on the corresponding physical parameters for a chosen model; after training, we carry out rapid parameter inferences. As this approach approximates the likelihood through variational inference, it yields results more efficiently. Through this innovative approach, we demonstrated that the total time, from training to parameter inference, is under $\approx3$h. We showed that for a given KN light curve, we can rapidly perform parameter inference based on the required model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17377 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder Saha, Surojit Kong, Albert K. H High Energy Astrophysical Phenomena The coalescence of binary neutron stars in the GW170817 event led to the generation of gravitational waves, accompanied by the electromagnetic counterpart known as a kilonova (KN). Since then, it has been a prime topic of interest, as it has provided much insight into multi-messenger astronomy. Apart from existing methods for parameter estimation, we propose an alternative technique for it, utilizing the strength and flexibility of a conditional variational autoencoder. Publicly available light curves are used as training data, conditioning on the corresponding physical parameters for a chosen model; after training, we carry out rapid parameter inferences. As this approach approximates the likelihood through variational inference, it yields results more efficiently. Through this innovative approach, we demonstrated that the total time, from training to parameter inference, is under $\approx3$h. We showed that for a given KN light curve, we can rapidly perform parameter inference based on the required model. |
| title | Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2605.17377 |