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Revisiting the mysterious origin of FRB 20121102A with machine-learning classification

Published online by Cambridge University Press:  12 November 2024

Leah Ya Ling Lin Open the ORCID record for Leah Ya Ling Lin [Opens in a new window] ,
Tetsuya Hashimoto Open the ORCID record for Tetsuya Hashimoto [Opens in a new window] ,
Tomotsugu Goto Open the ORCID record for Tomotsugu Goto [Opens in a new window] ,
Bjorn Jasper Raquel ,
Simon C.-C. Ho ,
Bo-Han Chen ,
Seong Jin Kim Open the ORCID record for Seong Jin Kim [Opens in a new window]  and
Chih-Teng Ling Open the ORCID record for Chih-Teng Ling [Opens in a new window]
Leah Ya Ling Lin
Affiliation:
Department of Physics, National Tsing Hua University, Hsinchu, Taiwan
Tetsuya Hashimoto*
Affiliation:
Department of Physics, National Chung Hsing University, Taichung, Taiwan
Tomotsugu Goto
Affiliation:
Department of Physics, National Tsing Hua University, Hsinchu, Taiwan Institute of Astronomy, National Tsing Hua University, Hsinchu, Taiwan
Bjorn Jasper Raquel
Affiliation:
Department of Physics, National Chung Hsing University, Taichung, Taiwan National Institute of Physics, University of the Philippines, Diliman, Quezon City, Philippines
Simon C.-C. Ho
Affiliation:
Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT, Australia Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Hawthorn, VIC, Australia OzGrav: The Australian Research Council Centre of Excellence for Gravitational Wave Discovery, Hawthorn, VIC, Australia ASTRO3D: ARC Centre of Excellence for All-sky Astrophysics in 3D, Canberra, ACT, Australia
Bo-Han Chen
Affiliation:
Graduate School of Data Science, Seoul National University, Gwanak-gu, Seoul, Korea
Seong Jin Kim
Affiliation:
Institute of Astronomy, National Tsing Hua University, Hsinchu, Taiwan
Chih-Teng Ling
Affiliation:
Institute of Astronomy, National Tsing Hua University, Hsinchu, Taiwan
*
Corresponding author: Tetsuya Hashimoto, Email: [email protected].
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Abstract

Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the square kilometer array or Bustling Universe Radio Survey Telescope in Taiwan discover more FRBs than before.

Keywords

Radio continuum: galaxiesmethods: datamethods: numericalmethods: analytical
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 41 , 2024 , e090
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia

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References

Campello, R. J. G. B., Moulavi, D., & Sander, J. 2013, in Advances in Knowledge Discovery and Data Mining, 160 (Berlin, Heidelberg: Springer Berlin Heidelberg). ISBN: 978-3-642-37456-2. https://doi.org/10.1007/978-3-642-37456-2_14.Google Scholar
Chatterjee, S., et al. 2017, Natur, 541, 58. https://doi.org/10.1038/nature20797. arXiv: 1701.01098 [astro-ph.HE].CrossRefGoogle Scholar
Chen, B. H., Hashimoto, T., Goto, T., Kim, S. J., Santos, D. J. D., On, A. Y. L., Lu, T.-Y., & Hsiao, T. Y.-Y. 2022, MNRAS, 509, 1227. https://doi.org/10.1093/mnras/stab2994.2110.09440 [astro-ph.HE].CrossRefGoogle Scholar
Chen, B. H., Hashimoto, T., Goto, T., Raquel, B. J. R., Uno, Y., Kim, S. J., Hsiao, T. Y.-Y., & Ho, S. C.-C. 2023, MNRAS, 521, 5738. https://doi.org/10.1093/mnras/stad930.CrossRefGoogle Scholar
Connor, L., & van Leeuwen, J. 2018, AJ, 156, 256. https://doi.org/10.3847/1538-3881/aae649.CrossRefGoogle Scholar
Cordes, J. M., Ocker, S. K., & Chatterjee, S. 2022, ApJ, 931, 88. https://doi.org/10.3847/1538-4357/ac6873 arXiv: 2108.01172 [astro-ph.HE].CrossRefGoogle Scholar
Dewdney, P. E., Hall, P. J., Schilizzi, R. T., & Lazio, T. J. L. W. 2009, IEEE Proc., 97, 1482. https://doi.org/10.1109/JPROC.2009.2021005.CrossRefGoogle Scholar
Hashimoto, T., et al. 2022, MNRAS, 511, 1961. https://doi.org/10.1093/mnras/stac065. arXiv: 2201.03574 [astro-ph.HE].CrossRefGoogle Scholar
Hashimoto, T., et al. 2020, MNRAS, 497, 4107. https://doi.org/10.1093/mnras/staa2238. arXiv: 2008.00007 [astro-ph.HE].CrossRefGoogle Scholar
Ho, S. C.-C., Hashimoto, T., Goto, T., Lin, Y.-W., Kim, S. J., Uno, Y., & Hsiao, T. Y.-Y. 2023, ApJ, 950, 53. https://doi.org/10.3847/1538-4357/accb9e. arXiv: 2304.04990 [astro-ph.HE].CrossRefGoogle Scholar
Hubert, L., & Arabie, P. 1985, JC, 2, 193. https://doi.org/10.1007/bf01908075.CrossRefGoogle Scholar
Jahns, J. N., et al. 2022, MNRAS, 519, 666. https://doi.org/10.1093/mnras/stac3446.CrossRefGoogle Scholar
Kim, S. J., Hashimoto, T., Chen, B. H., Goto, T., Ho, S.-m. C.-C., Yu-Yang Hsiao, T., Wong, Y. H. V., & Yamasaki, S. 2022, MNRAS, 514, 5987. https://doi.org/10.1093/mnras/stac1689. arXiv: 2206.11330 [astro-ph.CO].CrossRefGoogle Scholar
Li, D., et al. 2021, Natur, 598, 267. https://doi.org/10.1038/s41586-021-03878-5. arXiv: 2107.08205 [astro-ph.HE].CrossRefGoogle Scholar
Lin, H.-H., et al. 2022, PASP, 134, 094106. https://doi.org/10.1088/1538-3873/ac8f 71. arXiv: 2206.08983 [astro-ph.IM].CrossRefGoogle Scholar
Lorimer, D. R., Bailes, M., McLaughlin, M. A., Narkevic, D. J., & Crawford, F. 2007, Sci, 318, 777. https://doi.org/10.1126/science.1147532. arXiv: 0709.4301 [astro-ph].Google Scholar
Lu, W., & Kumar, P. 2018, MNRAS, 477, 2470. https://doi.org/10.1093/mnras/sty716.1710.10270 [astro-ph.HE].CrossRefGoogle Scholar
Lyutikov, M., Blandford, R. D., & Machabeli, G. 1999, MNRAS, 305, 338. https://doi.org/10.1046/j.1365-8711.1999.02443.x. arXiv: astro-ph/9806363 [astro-ph].CrossRefGoogle Scholar
Macquart, J.-P., & Ekers, R. D. 2018, MNRAS, 474, 1900. https://doi.org/10.1093/mnras/stx2825.CrossRefGoogle Scholar
Manchester, R. N., & Taylor, J. H. 1977, IrAJ, 13, 142.Google Scholar
McInnes, L., Healy, J., & Melville, J. 2018, arXiv e-prints: https://doi.org/10.48550/arXiv.1802.03426. arXiv: 1802.03426 [stat.ML].CrossRefGoogle Scholar
McInnes, L., Healy, J., Saul, N., & Großberger, L. 2018, JOSS, 3, 861. https://doi.org/10.21105/joss.00861.CrossRefGoogle Scholar
Metzger, B. D., Margalit, B., & Sironi, L. 2019, MNRAS, 485, 4091. https://doi.org/10.1093/mnras/stz700. arXiv: 1902.01866 [astro-ph.HE].CrossRefGoogle Scholar
Niu, C.-H., et al. 2022, Natur, 606, 873. https://doi.org/10.1038/s41586-022-04662-z.CrossRefGoogle Scholar
Phillips, J. A. 1992, ApJ, 385, 282. https://doi.org/10.1086/170936.CrossRefGoogle Scholar
Raquel, B. J., Hashimoto, T., Goto, T., Chen, B. H., Uno, Y., Hsiao, T. Y.-Y., Kim, S. J., & Ho, S. C.-C. 2023, MNRAS, 524, 1668. https://doi.org/10.1093/mnras/stad1942.CrossRefGoogle Scholar
Scholz, P., et al. 2016, ApJ, 833, 177. https://doi.org/10.3847/1538-4357/833/2/177. arXiv: 1603.08880 [astro-ph.HE].CrossRefGoogle Scholar
Spitler, L. G., et al. 2014, ApJ, 790, 101. https://doi.org/10.1088/0004-637X/790/2/101. arXiv: 1404.2934 [astro-ph.HE].CrossRefGoogle Scholar
Wang, P. F., Wang, C., & Han, J. L. 2012, MNRAS, 423, 2464. https://doi.org/10.1111/j.1365-2966.2012.21053.x. arXiv: 1404.2934 [astro-ph.HE].CrossRefGoogle Scholar
Xiao, D., & Dai, Z.-G. 2022, A&A, 657, L7. https://doi.org/10.1051/0004-6361/202142268. arXiv: 2112.12301 [astro-ph.HE].CrossRefGoogle Scholar
Yang, X., Zhang, S.-B., Wang, J.-S., & Wu, X.-F. 2023, MNRAS, 522, 4342. https://doi.org/10.1093/mnras/stad1304. arXiv: 2304.13912 [astro-ph.HE].CrossRefGoogle Scholar
Yang, Y.-P., & Zhang, B. 2018, ApJ, 868, 31. https://doi.org/10.3847/1538-4357/aae685. arXiv: 1712.02702 [astro-ph.HE].CrossRefGoogle Scholar
Yang, Y.-P., Zhu, J.-P., Zhang, B., & Wu, X.-F. 2020, ApJ, 901, L13. https://doi.org/10.3847/2041-8213/abb535. arXiv: 2006.03270 [astro-ph.HE].CrossRefGoogle Scholar
Zhang, B. 2023, RvMP, 95, 035005. https://doi.org/10.1103/RevModPhys.95.035005. arXiv: 2212.03972 [astro-ph.HE].CrossRefGoogle Scholar
Zhu-Ge, J.-M., Luo, J.-W., & Zhang, B. 2023, MNRAS, 519, 1823. https://doi.org/10.1093/mnras/stac3599. arXiv: 2210.02471 [astro-ph.HE].CrossRefGoogle Scholar