Precise measurement on the binding energy of hypertriton from the nuclear emulsion data using analysis with machine learning

Authors

  • Ayumi Kasagi HENP RIKEN, Gifu Univ
  • Enqiang Liu
  • Manami Nakagawa
  • Hiroyuki Ekawa
  • Junya Yoshida
  • Wenbo Dou
  • Abdul Muneem
  • Kazuma Nakazawa
  • Christophe Rappold
  • Nami Saito
  • Takehiko. R. Saito
  • Masato Taki
  • Yoshiki. K. Tanaka
  • He Wang

DOI:

https://doi.org/10.31349/SuplRevMexFis.3.0308122

Keywords:

hypernucleus, hypertriton, binding energy, nuclear emulsion, machine learning

Abstract

A machine learning model has been developed to search events of production and decay of a hypertriton in nuclear emulsion data, which is used for measuring the binding energy of the hypertriton at the best precision. The developed model employs an established technique for object detection and is trained with surrogate images generated by Monte Carlo simulations and image transfer techniques. The first hypertriton event has already been detected with the developed method only with 10−4 of the total emulsion data. It implies that a sufficient number of hypertriton events will soon be detected for the precise measurement of the hypertriton binding energy.

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Published

2022-06-01

How to Cite

1.
Kasagi A, Liu E, Nakagawa M, Ekawa H, Yoshida J, Dou W, Muneem A, Nakazawa K, Rappold C, Saito N, Saito T, Taki M, Tanaka Y, Wang H. Precise measurement on the binding energy of hypertriton from the nuclear emulsion data using analysis with machine learning. Supl. Rev. Mex. Fis. [Internet]. 2022 Jun. 1 [cited 2024 Dec. 21];3(3):0308122 1-6. Available from: https://rmf.smf.mx/ojs/index.php/rmf-s/article/view/6262

Issue

Vol. 3 No. 3 (2022): Suplemento de la Revista Mexicana de Física. 19th International conference on hadron spectroscopy and structure in memoriam Simon Eidelman

Section

Hadrons in hot an nuclear environment including hypernuclei

License

Copyright (c) 2022 Ayumi Kasagi, Enqiang Liu, Manami Nakagawa, Hiroyuki Ekawa, Junya Yoshida, Wenbo Dou, Abdul Muneem, Kazuma Nakazawa, Christophe Rappold, Nami Saito, Takehiko. R. Saito, Masato Taki, Yoshiki. K. Tanaka, He Wang (Author)

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