Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200

Abstract

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.

arXiv commentAs accepted by JINST

References in corpus (19)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Normalizing Flows: An Introduction and Review of Current Methods
• The Sudbury Neutrino Observatory
• Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset
• An improved measurement of the 2νββ half-life of Xe-136 with EXO-200
• NEXO: Neutrinoless double beta decay search beyond $10^{28}$ year half-life sensitivity
• The EXO-200 detector, part I: Detector design and construction
• Correlated Fluctuations between Luminescence and Ionization in Liquid Xenon
• Borexino calibrations: Hardware, Methods, and Results
• Observation of Anti-correlation between Scintillation and Ionization for MeV Gamma-Rays in Liquid Xenon
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Automated Calibration System for a High-Precision Measurement of Neutrino Mixing Angle ${\theta }_{13}$ with the Daya Bay Antineutrino Detectors
• Spectral Photon Sorting For Large-Scale Cherenkov and Scintillation Detectors
• Laser Calibration System in JUNO
• Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
• GPU-based optical simulation of the DARWIN detector
• Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers

Cited by in corpus (2)

• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Performance of an Optical TPC Geant4 Simulation with Opticks GPU-Accelerated Photon Propagation