Neutrino classification through deep learning amid the Hyper-Kamiokande project development

Abstract

Neutrinos are a type of elemental particle that are characterized by the fact that their mass is really small, that they have no electric charge and present a special behavior called oscillation in which they can be measured to be of a kind different to the one they actually are. All these characteristics make neutrinos one of the most studied particles by different researchers and in different facilities nowadays, since the information we can obtain from its study allows us to solve some of the Universe’s greatest mysteries. One of these projects where neutrinos are studied is the Hyper-Kamiokande which refers to both, the international collaboration of researchers, to which Mexico belongs to, and the neutrino grand-scale detector based on Cherenkov radiation currently being built in Japan. In this detector the data of a neutrino event is collected by a special kind of sensors located in its walls called Photo Multiplier Tubes or PMTs, to then be analyzed, and this analysis usually starts by the identification of the particles involved in an event, which is where this project comes forth, since an appropriate method to classify neutrinos based on the radiation pattern they leave as they pass through the detector is needed. Hence, in the following project to obtain the Master in Computer Science degree, we implement and test 4 deep learning architectures: VGG19, ResNet50, PointNet and Vision Transformer, for the classification of neutrinos since they are state of the art methods, this is, they are architectures used as the starting point for any classification task and, moreover, we can tune them and/or apply different techniques such as regularization to get the best possible performance while reducing overfitting. Using the mentioned architectures we process a dataset composed of neutrino events simulated by a software called WCSim in 2021. These events are of single ring type, correspond to the IWCD tank, a smaller tank being built to aid in the tasks of the Hyper-Kamiokande and range from 9 thousand to 8 million per each of the 3 particles considered in the project: muon and electron neutrinos and gamma particles. The results show that ResNet50 was the architecture that gave the best results while also minimizing the computational resources needed, though its performance is similar to the one given by VGG19 and PointNet, they require a larger time to process any dataset, whereas Vision Transformer provided the poorest results, however, all results improved by processing the largest datasets. Then, in comparison with a state of the art custom CNN we found that our highest average accuracy is within the same range as the one they obtained, whereas, in comparison with the ResNet50 model currently being used in the HK collaboration we found that the obtained AUC for the TPR signal (electron) vs FPR background (gamma) curve for our best model is 0.71, whereas this AUC value for the collaboration is 0.77, nonetheless, we have to consider that to obtain this value the whole results are not analyzed by the collaboration but cuts are applied and therefore, our results can be considered close.