BROWSE

Related Scientist

douglas,leonard's photo.

douglas,leonard
지하실험연구단
more info

ITEM VIEW & DOWNLOAD

Generative adversarial networks for scintillation signal simulation in EXO-200

Cited 0 time in webofscience Cited 0 time in scopus
39 Viewed 0 Downloaded
Title
Generative adversarial networks for scintillation signal simulation in EXO-200
Author(s)
Li, S.; Ostrovskiy, I.; Li, Z.; Yang, L.; AlAKharusi, 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.; DerMesrobian-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.; Krucken, R.; Kuchenkov, A.; Kumar, K.S.; Lan, Y.; Larson, A.; Lenardo, B.G.; D.S. Leonard; 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.; Retiere, 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.
Publication Date
2023-06
Journal
Journal of Instrumentation, v.18, no.6
Publisher
Institute of Physics
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.
URI
https://pr.ibs.re.kr/handle/8788114/14670
DOI
10.1088/1748-0221/18/06/P06005
ISSN
1748-0221
Appears in Collections:
Center for Underground Physics(지하실험 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse