Deep Learning-Based $^{14}$C Pile-Up Identification in the JUNO Experiment

TL;DR

This paper explores deep learning techniques, including convolutional and transformer models, to identify $^{14}$C pile-up events in the JUNO experiment, aiming to improve energy resolution crucial for neutrino mass ordering measurements.

Contribution

It introduces deep learning models specifically designed for $^{14}$C pile-up identification in JUNO, demonstrating promising performance improvements over traditional methods.

Findings

Deep learning models effectively identify $^{14}$C pile-up events.

Transformer models outperform convolutional models in accuracy.

Improved pile-up detection enhances energy resolution for neutrino measurements.

Abstract

Measuring neutrino mass ordering (NMO) poses a fundamental challenge in neutrino physics. To address this, the Jiangmen Underground Neutrino Observatory (JUNO) experiment is scheduled to commence data collection in late 2024, with the ambitious goal of determining the NMO at a 3-sigma confidence level within a span of 6 years. A key factor in achieving this is ensuring a high-quality energy resolution of positrons. However, the presence of residual $^{14}$C isotopes in the liquid scintillator introduces pile-up effects that can impact the positron energy resolution. Mitigating these pile-up effects requires the identification of pile-up events, which presents a significant challenge. The signal from $^{14}$C is considerably smaller compared to the positron signal, making its identification difficult. Additionally, the close event time and vertex between a positron and a $^{14}$C further compound the identification challenge. This contribution focuses on the application of deep learning models for the identification of $^{14}$C pile-up events. It encompasses a range of models, including convolution-based models and advanced transformer models. Through performance evaluation, it shows the deep learning-based methods is promising to identify the pile-up events.