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Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

dc.contributor.authorSurojit Saha
dc.contributor.authorMichael J. Williams
dc.contributor.authorLaurence Datrier
dc.contributor.authorFergus Hayes
dc.contributor.authorMatt Nicholl
dc.contributor.authorAlbert K. H. Kong
dc.contributor.authorMartin Hendry
dc.contributor.authorIK Siong Heng
dc.contributor.authorGavin P. Lamb
dc.contributor.authorEn-Tzu Lin
dc.contributor.authorDaniel Williams
dc.contributor.otherInstitute of Astronomy, National Tsing Hua University , Hsinchu City, Taiwan (ROC) ; surojitsaha@gapp.nthu.edu.tw, akong@gapp.nthu.edu.tw
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK; Institute of Cosmology and Gravitation, University of Portsmouth , Portsmouth PO1 3FX, UK
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK
dc.contributor.otherAstrophysics Research Centre, School of Mathematics and Physics, Queens University Belfast , Belfast BT7 1NN, UK
dc.contributor.otherInstitute of Astronomy, National Tsing Hua University , Hsinchu City, Taiwan (ROC) ; surojitsaha@gapp.nthu.edu.tw, akong@gapp.nthu.edu.tw
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK
dc.contributor.otherAstrophysics Research Institute, Liverpool John Moores University , IC2 Liverpool Science Park, 146 Brownlow Hill, Liverpool L3 5RF, UK
dc.contributor.otherInstitute of Astronomy, National Tsing Hua University , Hsinchu City, Taiwan (ROC) ; surojitsaha@gapp.nthu.edu.tw, akong@gapp.nthu.edu.tw
dc.contributor.otherInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, UK
dc.date.accessioned2024-01-23T18:23:53Z
dc.date.accessioned2025-10-08T09:25:18Z
dc.date.available2025-10-08T09:25:18Z
dc.date.issued01-01-2024
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40321
dc.description.abstractThe discovery of the optical counterpart, along with the gravitational waves (GWs) from GW170817, of the first binary neutron star merger has opened up a new era for multimessenger astrophysics. Combining the GW data with the optical counterpart, also known as AT 2017gfo and classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonovae has brought about a revolution in the domain of multimessenger astronomy, there has been only one kilonova from a GW-detected binary neutron star merger event confirmed so far, and this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once the CVAE is trained, the timescale for light-curve generation is of the order of a few milliseconds, which is a speedup of the generation of light curves by 1000 times as compared to the simulation. The mean squared error between the generated and original light curves is typically 0.015 with a maximum of 0.08 for each set of considered physical parameters, while having a maximum of ≈0.6 error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results.
dc.language.isoEN
dc.publisherIOP Publishing
dc.subject.lccAstrophysics
dc.titleRapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder
dc.typeArticle
dc.description.keywordsNeural networks
dc.description.keywordsNeutron stars
dc.description.keywordsCompact objects
dc.description.keywordsLight curves
dc.description.doi10.3847/1538-4357/ad02f4
dc.title.journalThe Astrophysical Journal
dc.identifier.e-issn1538-4357
dc.identifier.oaioai:doaj.org/journal:a582ebe10e4e454abfe9be898354b6d4
dc.journal.infoVolume 961, Issue 2


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