Artificial intelligence pioneers the double-strangeness factory

TL;DR

This paper demonstrates the use of AI, including generative models and machine learning, to identify and analyze double hypernuclei in nuclear emulsion, advancing research in hypernuclear physics and neutron star composition.

Contribution

It introduces an innovative AI-driven methodology combining generative AI, Monte Carlo simulations, and object detection to observe and analyze double hypernuclei in nuclear emulsion.

Findings

First observation of a double hypernucleus using AI techniques.

Determined the binding energy of two Λ hyperons as 25.57 MeV.

Measured the ΛΛ interaction energy as 2.83 MeV.

Abstract

Artificial intelligence (AI) is transforming not only our daily experiences but also the technological development landscape and scientific research. In this study, we pioneered the application of AI in double-strangeness hypernuclear studies. These studies which investigate quantum systems with strangeness via hyperon interactions provide insights into fundamental baryon-baryon interactions and contribute to our understanding of the nuclear force and composition of neutron star cores. Specifically, we report the observation of a double hypernucleus in nuclear emulsion achieved via innovative integration of machine learning techniques. The proposed methodology leverages generative AI and Monte Carlo simulations to produce training datasets combined with object detection AI for effective event identification. Based on the kinematic analysis and charge identification, the observed event was uniquely identified as the production and decay of resulting from {\Xi}- capture by 14N in the nuclear emulsion. Assuming capture in the atomic 3D state, the binding energy of the two {\Lambda} hyperons in 13B{\Lambda}{\Lambda}, B{\Lambda}{\Lambda}, was determined as 25.57 +- 1.18(stat.) +- 0.07(syst.) MeV. The {\Lambda}{\Lambda} interaction energy obtained was 2.83 +- 1.18(stat.) +- 0.14(syst.) MeV. This study marks a new era in double-strangeness research.