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Generative adversarial networks for scintillation signal simulation in EXO-200

Publication ,  Journal Article
Li, S; Ostrovskiy, I; Li, Z; Yang, L; Al Kharusi, S; Anton, G; Barbeau, PS; Badhrees, I; Beck, D; Belov, V; Bhatta, T; Breidenbach, M ...
Published in: Journal of Instrumentation
June 1, 2023

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.

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Published In

Journal of Instrumentation

DOI

EISSN

1748-0221

Publication Date

June 1, 2023

Volume

18

Issue

6

Related Subject Headings

  • Nuclear & Particles Physics
  • 51 Physical sciences
  • 40 Engineering
  • 09 Engineering
  • 02 Physical Sciences
 

Citation

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Li, S., Ostrovskiy, I., Li, Z., Yang, L., Al Kharusi, S., Anton, G., … Wrede, G. (2023). Generative adversarial networks for scintillation signal simulation in EXO-200. Journal of Instrumentation, 18(6). https://doi.org/10.1088/1748-0221/18/06/P06005
Li, S., I. Ostrovskiy, Z. Li, L. Yang, S. Al Kharusi, G. Anton, P. S. Barbeau, et al. “Generative adversarial networks for scintillation signal simulation in EXO-200.” Journal of Instrumentation 18, no. 6 (June 1, 2023). https://doi.org/10.1088/1748-0221/18/06/P06005.
Li S, Ostrovskiy I, Li Z, Yang L, Al Kharusi S, Anton G, et al. Generative adversarial networks for scintillation signal simulation in EXO-200. Journal of Instrumentation. 2023 Jun 1;18(6).
Li, S., et al. “Generative adversarial networks for scintillation signal simulation in EXO-200.” Journal of Instrumentation, vol. 18, no. 6, June 2023. Scopus, doi:10.1088/1748-0221/18/06/P06005.
Li S, Ostrovskiy I, Li Z, Yang L, Al Kharusi S, Anton G, Barbeau PS, Badhrees I, Beck D, Belov V, Bhatta T, Breidenbach M, Brunner T, Cao GF, Cen WR, Chambers C, Cleveland B, Coon M, Craycraft A, Daniels T, Darroch L, Daugherty SJ, Davis J, Delaquis S, Der Mesrobian-Kabakian A, DeVoe R, Dilling J, Dolgolenko A, Dolinski MJ, Echevers J, Fairbank W, Fairbank D, Farine J, Feyzbakhsh S, Fierlinger P, Fu YS, Fudenberg D, Gautam P, Gornea R, Gratta G, Hall C, Hansen EV, Hoessl J, Hufschmidt P, Hughes M, Iverson A, Jamil A, Jessiman C, Jewell MJ, Johnson A, Karelin A, Kaufman LJ, Koffas T, Krücken R, Kuchenkov A, Kumar KS, Lan Y, Larson A, Lenardo BG, Leonard DS, Li GS, Licciardi C, Lin YH, MacLellan R, McElroy T, Michel T, Mong B, Moore DC, Murray K, Njoya O, Nusair O, Odian A, Perna A, Piepke A, Pocar A, Retière F, Robinson AL, Rowson PC, Runge J, Schmidt S, Sinclair D, Skarpaas K, Soma AK, Stekhanov V, Tarka M, Thibado S, Todd J, Tolba T, Totev TI, Tsang R, Veenstra B, Veeraraghavan V, Vogel P, Vuilleumier JL, Wagenpfeil M, Watkins J, Weber M, Wen LJ, Wichoski U, Wrede G. Generative adversarial networks for scintillation signal simulation in EXO-200. Journal of Instrumentation. 2023 Jun 1;18(6).
Journal cover image

Published In

Journal of Instrumentation

DOI

EISSN

1748-0221

Publication Date

June 1, 2023

Volume

18

Issue

6

Related Subject Headings

  • Nuclear & Particles Physics
  • 51 Physical sciences
  • 40 Engineering
  • 09 Engineering
  • 02 Physical Sciences