| 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)
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| 2018 | |
| Source Publication | JOURNAL OF INSTRUMENTATION
 |
| ISSN | 1748-0221 |
| Volume | 13Pages:P08023 |
| Subtype | Article |
| Abstract | We 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. |
| Keyword | Analysis and statistical methods Double-beta decay detectors Pattern recognition cluster finding calibration and fitting methods Time projection chambers |
| DOI | 10.1088/1748-0221/13/08/P08023 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS Research Area | Instruments & Instrumentation |
| WOS Subject | Instruments & Instrumentation |
| WOS ID | WOS:000443201700003 |
| EI Accession Number | 20183805818528 |
| ADS Bibcode | 2018JInst..13P8023D |
| AXRIV CODE | 1804 |
| inspireid | 1670012 |
| Citation statistics |
Cited Times:20 [INSPIRE]
Cited Times:35 [ADS]
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| Document Type | 期刊论文 |
| Identifier | http://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 Affilication | Institute 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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