Binding energy of $^{3}_{\Lambda}\rm{H}$ and $^{4}_{\Lambda}\rm{H}$ via image analyses of nuclear emulsions using deep-learning
Abstract
Subatomic systems are pivotal for understanding fundamental baryonic interactions, as they provide direct access to quark-level degrees of freedom. In particular, introducing a strange quark adds "strangeness" as a new dimension, offering a powerful tool for exploring nuclear forces. The hypertriton, the lightest three-body hypernuclear system, provides an ideal testing ground for investigating baryonic interactions and quark behavior involving up, down, and strange quarks. However, experimental measurements of its lifetime and binding energy, key indicators of baryonic interactions, show significant deviations in results obtained from energetic collisions of heavy-ion beams. Identifying alternative pathways for precisely measuring the hypertriton's binding energy and lifetime is thus crucial for advancing experimental and theoretical nuclear physics. Here, we present an experimental study on the binding energies of (hypertriton) and , performed through the analysis of photographic nuclear emulsions using modern techniques. By incorporating deep-learning methods, we uncovered systematic uncertainties in conventional nuclear emulsion analyses and established a refined calibration protocol for determining binding energies accurately. Our results are independent of those obtained from heavy-ion collision experiments, offering a complementary measurement and opening new avenues for investigating few-body hypernuclei interactions.
Cite
@article{arxiv.2504.01601,
title = {Binding energy of $^{3}_{\Lambda}\rm{H}$ and $^{4}_{\Lambda}\rm{H}$ via image analyses of nuclear emulsions using deep-learning},
author = {Ayumi Kasagi and Takehiko R. Saito and Vasyl Drozd and Hiroyuki Ekawa and Samuel Escrig and Yiming Gao and Yan He and Enqiang Liu and Abdul Muneem and Manami Nakagawa and Kazuma Nakazawa and Christophe Rappold and Nami Saito and Masato Taki and Yoshiki K. Tanaka and He Wang and Ayari Yanai and Junya Yoshida and Masahiro Yoshimoto},
journal= {arXiv preprint arXiv:2504.01601},
year = {2025}
}