Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

TL;DR

This paper introduces a neural network and ensemble learning-based method for universal uncertainty estimation in nuclear detector signals, improving robustness and out-of-distribution detection in physical measurements.

Contribution

It presents a novel deep learning approach combined with ensemble methods for uncertainty estimation in nuclear signal analysis, addressing limitations of traditional techniques.

Findings

Effective in detecting out-of-distribution samples

Achieves high accuracy in time and energy measurements

Demonstrates universal applicability through simulation and real data

Abstract

Characterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the heterogeneous nature of uncertainty or inadequate to perform well with unknown mathematical models. In this paper, we propose using multi-layer convolutional neural networks for empirical uncertainty estimation and feature extraction of nuclear pulse signals. This method is based on deep learning, a recent development of machine learning techniques, which learns the desired mapping function from training data and generalizes to unseen test data. Furthermore, ensemble learning is utilized to estimate the uncertainty originating from trainable parameters of the network and to improve the robustness of the whole model. To evaluate the performance of the proposed method, simulation studies, in comparison with curve fitting, investigate extensive conditions and show its universal applicability. Finally, a case study is made using data from a NICA-MPD electromagnetic calorimeter module exposed to a test beam at DESY, Germany. The uncertainty estimation method successfully detected out-of-distribution samples and also achieved good accuracy in time and energy measurements.