Unique approach for precise determination of binding energies of hypernuclei with nuclear emulsion and machine learning

¹ High Energy Nuclear Physics Laboratory, Cluster for Pioneering Research, RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
² Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
³ Institute of Modern Physics, Chinese Academy of Sciences, 509 Nanchang Road, Lanzhou, Gansu Province, 730000, China
⁴ School of Nuclear Science and Technology, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China
⁵ Department of physics, Tohoku University, Aramaki, Aoba-ku, Sendai 980-8578, Japan
⁶ Department of Physics, Saitama University, Saitama, 338-8570, Japan
⁷ Faculty of Engineering Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, 23640, KP, Pakistan
⁸ Faculty of Education, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
⁹ Instituto de Estructura de la Materia, CSIC, Madrid, Spain
¹⁰ GSI Helmholtz Centre for Heavy Ion Research, Planckstrasse 1, D-64291 Darmstadt, Germany
¹¹ School of Nuclear Science and Technology, Lanzhou University, 222 South Tianshui Road, Lanzhou, Gansu Province, 730000, China
¹² Graduate School of Artificial Intelligence and Science, Rikkyo University, 3-34-1 Nishi Ikebukuro, Toshima-ku, Tokyo, 171-8501, Japan
¹³ RIKEN Nishina Center for Accelerator-Based Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

^* e-mail: manami.nakagawa@riken.jp

Published online: 9 November 2022

Abstract

Hypertriton is the lightest hypernucleus and a benchmark in hypernuclear physics. However, it has recently been suggested that its lifetime and binding energy values may differ from the established values. To solve this puzzle, it is necessary to measure both values with a higher precision. For the precise measurement of the binding energy, we are aiming at developing a novel technique to measure the hypertriton binding energy with unprecedented accuracy by combining nuclear emulsion data and machine learning techniques. The analysis will be based on the J-PARC E07 nuclear emulsion data. Furthermore, a machine-learning model is being developed to identify other single and double-strangeness hypernuclei.