Logo image
Back
Deep neural networks for energy and position reconstruction in EXO-200
Journal article   Open access   Peer reviewed

Deep neural networks for energy and position reconstruction in EXO-200

S. Delaquis, M. J. Jewell, I. Ostrovskiy, M. Weber, T. Ziegler, J. Dalmasson, L. J. Kaufman, T. Richards, J. B. Albert, G. Anton, …
Journal of instrumentation, v 13(8), pp P08023-P08023
01 Aug 2018
DOI: https://doi.org/10.1088/1748-0221/13/08/P08023
url
http://arxiv.org/abs/1804.09641View
Accepted (AM)Open Access (License Unspecified),  Open

Abstract

Instruments & Instrumentation Science & Technology Technology
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.

Metrics

12 Record Views
31 citations in Web of Science
35 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Instruments & Instrumentation
Logo image