Optimization of convolutional neural networks for background suppression in the PandaX-III experiment

Abstract

The tracks recorded by a gaseous detector provide a possibility for charged particle identification. For searching the neutrinoless double beta decay events of 136Xe in the PandaX-III experiment, we optimized the convolutional neural network based on the Monte Carlo simulation data to improve the signal-background discrimination power. EfficientNet is chosen as the baseline model and the optimization is performed by tuning the hyperparameters. In particular, the maximum discrimination power is achieved by optimizing the channel number of the top convolutional layer. In comparison with our previous work, the significance of discrimination has been improved by about 70%.

15 pages, 13 figures

References in corpus (7)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Toward the discovery of matter creation with neutrinoless double-beta decay
• Search for the Majorana Nature of Neutrinos in the Inverted Mass Ordering Region with KamLAND-Zen
• Final Result of the MAJORANA DEMONSTRATOR's Search for Neutrinoless Double-$\beta$ Decay in ${}^{76}$Ge
• Measurement of the ${}^{136}$Xe two-neutrino double beta decay half-life via direct background subtraction in NEXT
• REST-for-Physics, a ROOT-based framework for event oriented data analysis and combined Monte Carlo response
• Signal identification with Kalman Filter towards background-free neutrinoless double beta decay searches in gaseous detectors