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Deep neural networks for energy and position reconstruction in EXO-200
Delaquis, S; Jewell, MJ; Ostrovskiy, I; Weber, M; Ziegler, T; Dalmasson, J; Kaufman, LJ; Richards, T; Albert, JB; Anton, G; Badhrees, I; Barbeau, PS; Bayerlein, R; Beck, D; Belov, V; Breidenbach, M; Brunner, T; Cao, GF; Cen, WR; Chambers, C; Clevel; , B; Coon, M; Craycraft, A; Cree, W; Daniels, T; Danilov, M; Daugherty, SJ; Daughhetee, J; Davis, J; Mesrobian-Kabakian, AD; DeVoe, R; Dilling, J; Dolgolenko, A; Dolinski, MJ; Fairbank, W; Farine, J; Feyzbakhsh, S; Fierlinger, P; Fudenberg, D; Gornea, R; Gratta, G; Hall, C; Hansen, EV; Harris, D; Hoessl, J; Hufschmidt, P; Hughes, M; Iverson, A; Jamil, A; Johnson, A; Karelin, A; Koffas, T; Kravitz, S; Krucken, R; Kuchenkov, A; Kumar, KS; Lan, Y; Leonard, DS; Li, GS; Li, S; Licciardi, C; Lin, YH; MacLellan, R; Michel, T; Mong, B; Moore, D; Murray, K; Njoya, O; Odian, A; Piepke, A; Pocar, A; Retiere, F; Robinson, AL; Rowson, PC; Schmidt, S; Schubert, A; Sinclair, D; Soma, AK; Stekhanov, V; Tarka, M; Todd, J; Tolba, T; Veeraraghavan, V; Vuilleumier, JL; Wagenpfeil, M; Waite, A; Watkins, J; Wen, LJ; Wichoski, U; Wrede, G; Xia, Q; Yang, L; Yen, YR; Zeldovich, OY; Cao GF(曹国富); Cen WR(岑吴镕); Wen LJ(温良剑)
2018
Source PublicationJOURNAL OF INSTRUMENTATION
ISSN1748-0221
Volume13Pages:P08023
SubtypeArticle
AbstractWe 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.
KeywordAnalysis and statistical methods Double-beta decay detectors Pattern recognition cluster finding calibration and fitting methods Time projection chambers
DOI10.1088/1748-0221/13/08/P08023
Indexed BySCI ; EI
Language英语
WOS Research AreaInstruments & Instrumentation
WOS SubjectInstruments & Instrumentation
WOS IDWOS:000443201700003
EI Accession Number20183805818528
ADS Bibcode2018JInst..13P8023D
AXRIV CODE1804
inspireid1670012
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Cited Times:13 [INSPIRE]
Cited Times:12 [ADS]
Document Type期刊论文
Identifierhttp://ir.ihep.ac.cn/handle/311005/286252
Collection实验物理中心
粒子天体物理中心
Affiliation中国科学院高能物理研究所
First Author AffilicationInstitute of High Energy
Recommended Citation
GB/T 7714
Delaquis, S,Jewell, MJ,Ostrovskiy, I,et al. Deep neural networks for energy and position reconstruction in EXO-200[J]. JOURNAL OF INSTRUMENTATION,2018,13:P08023.
APA Delaquis, S.,Jewell, MJ.,Ostrovskiy, I.,Weber, M.,Ziegler, T.,...&温良剑.(2018).Deep neural networks for energy and position reconstruction in EXO-200.JOURNAL OF INSTRUMENTATION,13,P08023.
MLA Delaquis, S,et al."Deep neural networks for energy and position reconstruction in EXO-200".JOURNAL OF INSTRUMENTATION 13(2018):P08023.
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