A novel machine learning method to detect double-$\Lambda$ hypernuclear events in nuclear emulsions

TL;DR

This paper introduces a machine learning approach using Mask R-CNN to efficiently detect double-$\\Lambda$ hypernuclear events in nuclear emulsions, significantly reducing manual inspection time and identifying new candidate events.

Contribution

The study presents a novel machine learning method trained on simulated data to detect double-$\Lambda$ hypernuclear events, including the Nagara event, with high efficiency and reduced manual effort.

Findings

Achieved 93.8% detection efficiency for $^{6}_{\Lambda\Lambda}$He

Detected six new double-$\Lambda$ hypernuclear candidates in a small emulsion subset

Reduced manual inspection time by a factor of 500

Abstract

A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{\Lambda\Lambda}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$\Lambda$ hypernuclear event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$\Lambda$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$\Lambda$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.