Generative adversarial networks for scintillation signal simulation in EXO-200

Journal Article · 05 June 2023 · Journal of Instrumentation

DOI: https://doi.org/10.1088/1748-0221/18/06/P06005 · OSTI ID:2998520

Li, S. ^[1]; Ostrovskiy, I.; Li, Z.; Yang, L.; Al Kharusi, S.; Anton, G.; Barbeau, P.S.; Badhrees, I.; Beck, D.; Belov, V.; Bhatta, T.; Breidenbach, M.; Brunner, T.; Cao, G.F.; Cen, W.R.; Chambers, C.; Cleveland, B.; Coon, M.; Craycraft, A.; Daniels, T. ; Darroch, L.; Daugherty, S.J.; Davis, J.; Delaquis, S.; Der Mesrobian-Kabakian, A.; DeVoe, R.; Dilling, J.; Dolgolenko, A.; Dolinski, M.J.; Echevers, J.; Fairbank, W.; Fairbank, D.; Farine, J.; Feyzbakhsh, S.; Fierlinger, P.; Fu, Y.S.; Fudenberg, D.; Gautam, P.; Gornea, R.; Gratta, G.; Hall, C.; Hansen, E.V.; Hoessl, J.; Hufschmidt, P.; Hughes, M.; Iverson, A.; Jamil, A.; Jessiman, C.; Jewell, M.J.; Johnson, A.; Karelin, A.; Kaufman, L.J.; Koffas, T.; Krücken, R.; Kuchenkov, A.; Kumar, K.S.; Lan, Y.; Larson, A.; Lenardo, B.G.; Leonard, D.S.; Li, G.S.; Licciardi, C.; Lin, Y.H.; MacLellan, R.; McElroy, T.; Michel, T.; Mong, B.; Moore, D.C.; Murray, K.; Njoya, O.; Nusair, O.; Odian, A.; Perna, A.; Piepke, A.; Pocar, A.; Retière, F.; Robinson, A.L.; Rowson, P.C.; Runge, J.; Schmidt, S.; Sinclair, D.; Skarpaas, K.; Soma, A.K.; Stekhanov, V.; Tarka, M.; Thibado, S.; Todd, J.; Tolba, T.; Totev, T.I.; Tsang, R.; Veenstra, B.; Veeraraghavan, V.; Vogel, P.; Vuilleumier, J.-L.; Wagenpfeil, M.; Watkins, J.; Weber, M.; Wen, L.J.; Wichoski, U.; Wrede, G.; Wu, S.X.; Xia, Q.; Yahne, D.R.; Yen, Y.-R.; Zeldovich, O.Ya.; Ziegler, T.

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1. University of Illinois, Urbana-Champaign, IL (United States); et al.

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. Furthermore, 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.

• Research Organization: Stanford Univ., CA (United States); Stanford University, CA (United States); Univ. of Alabama, Tuscaloosa, AL (United States)
• Sponsoring Organization: USDOE; USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Nuclear Physics (NP)
• Contributing Organization: EXO-200 Collaboration
• Grant/Contract Number: SC0017970; SC0019261
• OSTI ID: 2998520
• Journal Information: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 06 Vol. 18; ISSN 1748-0221
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United States
• Language: English