GAMMA-RAY BURST LIGHT CURVE RECONSTRUCTION WITH PREDICTIVE MODELS

A. Zhunuskanov; A. Sakan; A. Akhmetali; M. Zaidyn; N. Ussipov

doi:10.31489/2025N4/132-142

Authors

DOI:

https://doi.org/10.31489/2025N4/132-142

Keywords:

Gamma-ray burst, deep learning, neural networks, light curve

Abstract

Gamma-ray bursts represent some of the most energetic and complex phenomena in the universe, characterized by highly variable light curves that often contain observational gaps. Reconstructing these light curves is essential for gaining deeper insight into the physical processes driving such events. This study proposes a machine learning-based framework for the reconstruction of gamma-ray burst light curves, focusing specifically on the plateau phase observed in X-ray data. The analysis compares the performance of three sequential modeling approaches: a bidirectional recurrent neural network, a gated recurrent architecture, and a convolutional model designed for temporal data. The findings of this study indicate that the Bidirectional Gated Recurrent Unit model showed the best predictive accuracy among the evaluated models across all gamma-ray burst types, as measured by Mean Absolute Error, Root Mean Square Error, and Coefficient of Determination. Notably, Bidirectional Gated Recurrent Unit exhibited enhanced capability in modeling both gradual plateau phases and abrupt transient features, including flares and breaks, particularly in complex light-curve scenarios.

References

Kumar P., Zhang B. (2015) The physics of gamma-ray bursts & relativistic jets. Physics Reports, 561, 1-109. https://doi.org/10.1016/j.physrep.2014.09.008. DOI: https://doi.org/10.1016/j.physrep.2014.09.008

Meegan C.A., Fishman G.J., Wilson R.B., Paciesas W.S., Pendleton G.N., Horack J.M., Kouveliotou C. (1992) Spatial distribution of γ-ray bursts observed by BATSE. Nature, 355 (6356), 143-145. https://doi.org/10.1038/355143a0. DOI: https://doi.org/10.1038/355143a0

Barthelmy S.D., Chincarini G., Burrows D.N., Gehrels N., Covino S., Moretti A., Wijers R.A.M.J. (2005) An origin for short γ-ray bursts unassociated with current star formation. Nature, 438(7070), 994–996, https://doi.org/10.1038/nature04392 DOI: https://doi.org/10.1038/nature04392

Burrows D.N., Romano P., Falcone A., Kobayashi S., Zhang B., Moretti A., Gehrels N. (2005) Bright X-ray flares in gamma-ray burst afterglows. Science, 309(5742), 1833-1835. https://doi.org/10.1126/science.1116168. DOI: https://doi.org/10.1126/science.1116168

Boella G., Butler R. C., Perola G. C., Piro L., Scarsi L., Bleeker J. A. M. (1997) BeppoSAX, the wide band mission for X-ray astronomy. Astronomy and Astrophysics Supplement Series, 122(2), 299-307. https://doi.org/10.1051/aas:1997136. DOI: https://doi.org/10.1051/aas:1997136

Ricker G.R., Vanderspek R.K. (2003) Gamma-ray burst and afterglow astronomy 2001: A workshop celebrating the first year of the hete mission. In Gamma-Ray Burst and Afterglow Astronomy 2001: A Workshop Celebrating the First Year of the HETE Mission, 662. https://doi.org/10.1051/0004-6361%3A200809709.

Willingale R., O’brien P.T., Osborne J. P., Godet O., Page K. L., Goad M.R., Chincarini G. (2007) Testing the standard fireball model of gamma-ray bursts using late X-ray afterglows measured by Swift. The Astrophysical Journal, 662(2), 1093. https://doi.org/10.1086/517989. DOI: https://doi.org/10.1086/517989

Zhang Z. B., Choi C.S. (2008) An analysis of the durations of Swift gamma-ray bursts. Astronomy & Astrophysics, 484(2), 293-297. https://doi.org/10.1051/0004-6361:20079210. DOI: https://doi.org/10.1051/0004-6361:20079210

Sourav S., Shukla A., Dwivedi R., Singh K. (2023) Predicting Missing Light Curves of Gamma-Ray Bursts with Bidirectional-LSTM: An Approach for Enhanced Analysis. Available at: https://arxiv.org/abs/2310.02602.

Wang F., Zou Y. C., Liu F., Liao B., Liu Y., Chai Y., Xia L. (2020) A comprehensive statistical study of gamma-ray bursts. The Astrophysical Journal, 893(1), 77. https://doi.org/10.48550/arXiv.1902.05489. DOI: https://doi.org/10.3847/1538-4357/ab0a86

Abdikamalov E., Beniamini P. (2025) Reverse and forward shock afterglow emission from steep jets viewed off-axis. Monthly Notices of the Royal Astronomical Society, 539(3), 2707-2717. https://doi.org/10.1093/mnras/staf649 DOI: https://doi.org/10.1093/mnras/staf649

Komesh T., Grossan B., Maksut Z., Abdikamalov E., Krugov M., Smoot G.F. (2023) Evolution of the afterglow optical spectral shape of GRB 201015A in the first hour: evidence for dust destruction. Monthly Notices of the Royal Astronomical Society, 520(4), 6104-6110. https://doi.org/10.1093/mnras/stad538 DOI: https://doi.org/10.1093/mnras/stad538

Gritsevich M., Castro-Tirado A. J., Kubánek P., Pandey S.B., Hiriart D. (Eds.) (2025). Early-time optical spectral shape measurements of GRB 200925B. In VII Workshop on Robotic Autonomous Observatories (Revista Mexicana de Astronomía y Astrofísica, Serie de Conferencias, 59, 109–113. https://doi.org/10.22201/ia.14052059p.2025.59.20

Baron D. (2019) Machine learning in astronomy: A practical overview. arXiv preprint arXiv:1904.07248. https://doi.org/10.48550/arXiv.1904.07248

Fluke C. J., Jacobs C. (2020) Surveying the reach and maturity of machine learning and artificial intelligence in astronomy. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1349. https://doi.org/10.48550/arXiv.1912.02934. DOI: https://doi.org/10.1002/widm.1349

Zhang H., Wang J., Zhang Y., Du X., Wu H., Zhang T. (2023) Review of artificial intelligence applications in astronomical data processing. Astronomical Techniques and Instruments, 1(1), 1-15. https://doi.org/10.61977/ati2024001. DOI: https://doi.org/10.61977/ati2024001

Ussipov N., Zhanabaev Z., Almat A., Zaidyn M., Turlykozhayeva D., Akniyazova A., Namazbayev T. (2024) Classification of Gravitational Waves from Black Hole-Neutron Star Mergers with Machine Learning. Journal of Astronomy and Space Sciences, 41(3), 149-158. https://doi.org/10.5140/JASS.2024.41.3.149. DOI: https://doi.org/10.5140/JASS.2024.41.3.149

Akhmetali A., Namazbayev T., Subebekova G., Zaidyn M., Akniyazova A., Ashimov Y., Ussipov N. (2024) Classification of Variable Star Light Curves with Convolutional Neural Network. Galaxies, 12(6), 75. https://doi.org/10.3390/galaxies12060075. DOI: https://doi.org/10.3390/galaxies12060075

Ussipov N., Akhtanov S., Zhanabaev Z., Turlykozhayeva D., Karibayev B., Namazbayev T., Tang X. (2024) Automatic modulation classification for MIMO system based on the mutual information feature extraction. IEEE Access, 12, 68463-68470. https://doi.org/10.1109/ACCESS.2024.3400448. DOI: https://doi.org/10.1109/ACCESS.2024.3400448

Akhmetali A., Zhunuskanov A., Sakan A., Zaidyn M., Namazbayev T., Turlykozhayeva D., Ussipov N. (2025) Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis. https://doi.org/10.48550/arXiv.2504.10038.

VanderPlas J., Connolly A. J., Ivezić Ž., Gray A. (2012) Introduction to astroML: Machine learning for astrophysics. Proceeding of the 2012 conference on intelligent data understanding, IEEE, 47-54. https://doi.org/10.1109/CIDU.2012.6382200. DOI: https://doi.org/10.1109/CIDU.2012.6382200

Turmaganbet U., Zhexebay D., Turlykozhayeva D., Skabylov A., Akhtanov S., Temesheva S., Masalim P., Tao M. (2025). Eurasian Physical Technical Journal, 22(2 (52), 121–132. https://doi.org/10.31489/2025N2/121-132. DOI: https://doi.org/10.31489/2025N2/121-132

Kruiswijk K., de Wasseige G. (2023). The classification and categorisation of gamma-ray bursts with machine learning techniques for neutrino detection. arXiv preprint arXiv:2308.12672. https://doi.org/10.48550/ arXiv.2308.12672 DOI: https://doi.org/10.22323/1.444.1508

Zhang P., Li B., Gui R., Xiong S., Zou Z.C., Wang X., Zhao H. (2024) Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data. The Astrophysical Journal Supplement Series, 272(1), 4. https://doi.org/10.48550/arXiv.2303.00370. DOI: https://doi.org/10.3847/1538-4365/ad2de5

Mehta N., Iyyani S. (2024) Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi-and BATSE-detected Gamma-Ray Bursts. The Astrophysical Journal, 969(2), 88. https://doi.org/10.3847/1538–4357/ad43e7. DOI: https://doi.org/10.3847/1538-4357/ad43e7

Kumar A., Sharma K., Vinkó J., Steeghs D., Gompertz B., Lyman J., Pursiainen M. (2024) Magnetars as powering sources of gamma-ray burst associated supernovae, and unsupervised clustering of cosmic explosions. Monthly Notices of the Royal Astronomical Society, 531(3), 3297-3309. https://doi.org/10.48550/arXiv.2403.18076. DOI: https://doi.org/10.1093/mnras/stae901

Garcia-Cifuentes K., Becerra R.L., De Colle F. (2024) ClassiPyGRB: Machine Learning-Based Classification and Visualization of Gamma Ray Bursts using t-SNE. arXiv preprint arXiv:2404.06439, https://arxiv.org/abs/2404.06439 DOI: https://doi.org/10.21105/joss.05923

Misra K., Arun K.G. (2024) Diversity in Fermi/GBM Gamma-Ray Bursts: New Insights from Machine Learning. The Astrophysical Journal, 974(1), 55. https://doi.org/10.3847/1538–4357/ad6d6a. DOI: https://doi.org/10.3847/1538-4357/ad6d6a

Chen J. M., Zhu K.R., Peng Z. Y., Zhang L. (2024) Unsupervised machine learning classification of Fermi gamma-ray bursts using spectral parameters. Monthly Notices of the Royal Astronomical Society, 527(2), 4272-4284. https://doi.org/10.1093/mnras/stad3407. DOI: https://doi.org/10.1093/mnras/stad3407

Zhu S.Y., Sun W.P., Ma D.L., Zhang,F.W. (2024) Classification of Fermi gamma-ray bursts based on machine learning. Monthly Notices of the Royal Astronomical Society, 532(2), 1434-1443. https://doi.org/10.48550/arXiv.2406.05357. DOI: https://doi.org/10.1093/mnras/stae1594

Dainotti M.G., Sharma R., Narendra A., Levine D., Rinaldi E., Pollo A., Bhatta G. (2023) A stochastic approach to reconstruct gamma-ray-burst light curves. The Astrophysical Journal Supplement Series, 267(2), 42. https://arxiv.org/abs/2304.00520. DOI: https://doi.org/10.3847/1538-4365/acdd07

Falco R., Parmiggiani N., Bulgarelli A., Panebianco G., Castaldini L., Di Piano A., Tavani M. (2025). A New Deep Learning Model for Gamma-Ray Bursts' Light Curves Simulation. In Astronomical Society of the Pacific Conference Series , 541, 230. https://doi.org/10.26624/BXNK3217

Bai S., Kolter J.Z., Koltun V. (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. https://doi.org/10.48550/arXiv.1803.01271.

GAMMA-RAY BURST LIGHT CURVE RECONSTRUCTION WITH PREDICTIVE MODELS