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Preprints of the Keldysh Institute of Applied Mathematics, 2021, 088, 11 pp.
DOI: https://doi.org/10.20948/prepr-2021-88
(Mi ipmp3005)
 

This article is cited in 1 scientific paper (total in 1 paper)

The determination of the supernovae parameters from their light curves using the machine learning

E. M. Urvachev
Full-text PDF (954 kB) Citations (1)
References:
Abstract: The paper discusses the application of the machine learning library, CatBoost, to determine the masses of radioactive isotopes from the supernova light curve at a later epochs. The synthetic light curve model used for the demonstration is based on the contribution of the five major radioactive decay chains starting with $^{56}$Ni, $^{57}$Ni, $^{44}$Ti, $^{22}$Na, $^{60}$Co. Separately, we considered sets of random light curves calculated for different isotope masses of both the three dominant chains ($^{56}$Ni, $^{57}$Ni, $^{44}$Ti) and all five. It is shown that the masses of dominant isotopes are determined with acceptable accuracy in both cases, even with the standard settings of the machine learning algorithm. In the second case, the accuracy of determining the masses of the other two isotopes ($^{22}$Na, $^{60}$Co) turns out to be unsatisfactory, probably due to their weak contribution to the total light curve.
Keywords: machine learning, supernovae, light curves.
Funding agency Grant number
Russian Science Foundation 21-11-00362
Document Type: Preprint
Language: Russian
Citation: E. M. Urvachev, “The determination of the supernovae parameters from their light curves using the machine learning”, Keldysh Institute preprints, 2021, 088, 11 pp.
Citation in format AMSBIB
\Bibitem{Urv21}
\by E.~M.~Urvachev
\paper The determination of the supernovae parameters from their light curves using the machine learning
\jour Keldysh Institute preprints
\yr 2021
\papernumber 088
\totalpages 11
\mathnet{http://mi.mathnet.ru/ipmp3005}
\crossref{https://doi.org/10.20948/prepr-2021-88}
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  • https://www.mathnet.ru/eng/ipmp/y2021/p88
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Препринты Института прикладной математики им. М. В. Келдыша РАН
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    Abstract page:60
    Full-text PDF :23
    References:17
     
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