Hunting hypernuclei by machine learning in nuclear emulsions

A hypernuclus is a subatomic systems with strange quark(s). They have been studied already for seven decades for understanding the fundamental baryonic interaction and nuclear matters inside the core of neutron stars. The hypertriton is the lightest hypernucleus with a neutron, a proton and a Lambda hyperon, and it is the benchmark in hypernuclear studies. However, recent experimental studies with heavy ion beams have revealed that the nature of the hypertriton is unclear, especially on its biding energy and lifetime. The most urgent issue is to measure its binding energy very precisely. Measurements with nuclear emulsion have provided the best precision for the hypernuclear binding energy, however, it requires a huge human load on visual image analyses. We have developed machine learning models to detect events associated with production and decay of hypertriton in nuclear emulsions data, and we have already discovered hypertriton events [1]. In the seminar, we’ll discuss the challenges and developments of our machine learning models as well as the outcomes and perspectives of our works.