Deep neural networks for energy and position reconstruction in EXO-200

Cited 0 time in webofscience Cited 0 time in scopus
11 Viewed 4 Downloaded
Title
Deep neural networks for energy and position reconstruction in EXO-200
Author(s)
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; Cleveland, 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; Douglas S. Leonard; 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
Publication Date
2018-08
Journal
Journal of Instrumentation, v.13, no.8, pp.p08023 -
Publisher
IOP PUBLISHING LTD
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. (c) 2018 IOP Publishing Ltd and Sissa Medialab
URI
https://pr.ibs.re.kr/handle/8788114/5222
ISSN
1748-0221
Appears in Collections:
Center for Underground Physics(지하실험 연구단) > Journal Papers (저널논문)
Files in This Item:
pdf1.pdfDownload

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