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

Publication ,  Journal Article
Delaquis, S; Jewell, MJ; Ostrovskiy, I; Weber, M; Ziegler, T; Dalmasson, J; Kaufman, LJ; Richards, T; Albert, JB; Anton, G; Badhrees, I; Li, S ...
Published in: Journal of Instrumentation
August 29, 2018

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.

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Published In

Journal of Instrumentation

DOI

EISSN

1748-0221

Publication Date

August 29, 2018

Volume

13

Issue

8

Related Subject Headings

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

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Delaquis, S., Jewell, M. J., Ostrovskiy, I., Weber, M., Ziegler, T., Dalmasson, J., … Zeldovich, O. Y. (2018). Deep neural networks for energy and position reconstruction in EXO-200. Journal of Instrumentation, 13(8). https://doi.org/10.1088/1748-0221/13/08/P08023
Delaquis, S., M. J. Jewell, I. Ostrovskiy, M. Weber, T. Ziegler, J. Dalmasson, L. J. Kaufman, et al. “Deep neural networks for energy and position reconstruction in EXO-200.” Journal of Instrumentation 13, no. 8 (August 29, 2018). https://doi.org/10.1088/1748-0221/13/08/P08023.
Delaquis S, Jewell MJ, Ostrovskiy I, Weber M, Ziegler T, Dalmasson J, et al. Deep neural networks for energy and position reconstruction in EXO-200. Journal of Instrumentation. 2018 Aug 29;13(8).
Delaquis, S., et al. “Deep neural networks for energy and position reconstruction in EXO-200.” Journal of Instrumentation, vol. 13, no. 8, Aug. 2018. Scopus, doi:10.1088/1748-0221/13/08/P08023.
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, Krücken 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, Retière 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. Deep neural networks for energy and position reconstruction in EXO-200. Journal of Instrumentation. 2018 Aug 29;13(8).
Journal cover image

Published In

Journal of Instrumentation

DOI

EISSN

1748-0221

Publication Date

August 29, 2018

Volume

13

Issue

8

Related Subject Headings

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