Saha, S. et al. (2024) Rapid generation of kilonova light curves using conditional variational autoencoder. Astrophysical Journal, 961(2), 165. (doi: 10.3847/1538-4357/ad02f4)
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Abstract
The 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.
| Item Type: | Articles |
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| Additional Information: | This work is supported by the National Science and Technology Council of Taiwan under the grants 110-2628- M-007-005 and 111-2112-M-007-020, and by a joint grant of the National Science and Technology Council and the Royal Society of Edinburgh through 110-2927-I-007-513. M.J.W. is supported by the Science and Technology Facilities Council [2285031,ST/V005634/1,ST/V005715/1]. I.K.S.H. is supported by the Science and Technology Research Council [ST/ L000946/1]. I.K.S.H. and M.J.W. are also supported by the European Cooperation in Science and Technology (COST) action [CA17137]. M.N. is supported by the European Research Council under the European Unionʼs Horizon 2020 research and innovation program (grant agreement No. 948381) and by funding from the UK Space Agency. |
| Keywords: | Compact objects, Neutron stars, Neural networks, Light curves |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Heng, Professor Ik Siong and Williams, Dr Daniel and Datrier, Ms Laurence and Hayes, Dr Fergus and Williams, Mr Michael and Hendry, Professor Martin |
| Authors: | Saha, S., Williams, M. J., Datrier, L., Hayes, F., Nicholl, M., Kong, A. K. H., Hendry, M., Heng, I. S., Lamb, G. P., Lin, E.-T., and Williams, D. |
| College/School: | College of Science and Engineering > School of Physics and Astronomy |
| Research Centre: | College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research |
| Journal Name: | Astrophysical Journal |
| Publisher: | IOP Publishing for the American Astronomical Society |
| ISSN: | 0004-637X |
| ISSN (Online): | 1538-4357 |
| Copyright Holders: | Copyright: © 2024 The Author(s) |
| First Published: | First published in Astrophysical Journal 961(2): 165 |
| Publisher Policy: | Reproduced under a Creative Commons licence |
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