Deep Neural Networks for Energy and Position Reconstruction in EXO-200

TL;DR

This paper demonstrates that deep neural networks can accurately reconstruct energy and position from raw detector data in the EXO-200 experiment, matching or surpassing traditional methods, and introduces a novel training approach using real data.

Contribution

It presents a new application of DNNs for direct parameter reconstruction from raw data and a unique training method that reduces reliance on Monte Carlo simulations.

Findings

DNNs achieve or exceed traditional reconstruction accuracy.

First evaluation of DNN algorithms on real detector calibration data.

Training on experimental data reduces dependence on Monte Carlo simulations.

Abstract

We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.