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

S. Delaquis; M. J. Jewell; I. Ostrovskiy; M. Weber; T. Ziegler; J. Dalmasson; L. J. Kaufman; T. Richards; J. B. Albert; G. Anton; I. Badhrees; P. S. Barbeau; R. Bayerlein; D. Beck; V. Belov; M. Breidenbach; T. Brunner; G. F. Cao; W. R. Cen; C. Chambers; B. Cleveland; M. Coon; A. Craycraft; W. Cree; T. Daniels; M. Danilov; S. J. Daugherty; J. Daughhetee; J. Davis; A. Der Mesrobian-Kabakian; R. DeVoe; J. Dilling; A. Dolgolenko; M. J. Dolinski; W. Fairbank; J. Farine; S. Feyzbakhsh; P. Fierlinger; D. Fudenberg; R. Gornea; G. Gratta; C. Hall; E. V. Hansen; D. Harris; J. Hoessl; P. Hufschmidt; M. Hughes; A. Iverson; A. Jamil; A. Johnson; A. Karelin; T. Koffas; S. Kravitz; R. Krucken; A. Kuchenkov; K. S. Kumar; Y. Lan; D. S. Leonard; G. S. Li; S. Li; C. Licciardi; Y. H. Lin; R. MacLellan; T. Michel; B. Mong; D. Moore; K. Murray; O. Njoya; A. Odian; A. Piepke; A. Pocar; F. Retiere; A. L. Robinson; P. C. Rowson; S. Schmidt; A. Schubert; D. Sinclair; A. K. Soma; V. Stekhanov; M. Tarka; J. Todd; T. Tolba; V. Veeraraghavan; J. -L. Vuilleumier; M. Wagenpfeil; A. Waite; J. Watkins; L. J. Wen; U. Wichoski; G. Wrede; Q. Xia; L. Yang; Y. -R. Yen; O. Ya Zeldovich; SLAC National Accelerator Lab., Menlo Park, CA (United States)

doi:10.1088/1748-0221/13/08/P08023

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

Journal article

Peer reviewed

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

S. Delaquis, M. J. Jewell, I. Ostrovskiy, M. Weber, T. Ziegler, J. Dalmasson, L. J. Kaufman, T. Richards, J. B. Albert, G. Anton, …

Journal of instrumentation, Vol.13(8), pp.P08023-P08023

29/08/2018

DOI: https://doi.org/10.1088/1748-0221/13/08/P08023

Abstract

Instruments & Instrumentation

Science & Technology

Technology

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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Title: Deep neural networks for energy and position reconstruction in EXO-200
Creators - without role: S. Delaquis - SLAC National Accelerator Laboratory
M. J. Jewell - Stanford University
I. Ostrovskiy - University of Alabama
M. Weber - Stanford University
T. Ziegler - Friedrich-Alexander-Universität Erlangen-Nürnberg
J. Dalmasson - Stanford University
L. J. Kaufman - Indiana University
T. Richards - University of Alabama
J. B. Albert - Indiana University
G. Anton - Friedrich-Alexander-Universität Erlangen-Nürnberg
I. Badhrees - Carleton University
P. S. Barbeau - Duke University
R. Bayerlein - Friedrich-Alexander-Universität Erlangen-Nürnberg
D. Beck - University of Illinois Urbana-Champaign
V. Belov - Inst Theoret & Expt Phys, Moscow, Russia
M. Breidenbach - SLAC National Accelerator Laboratory
T. Brunner - McGill University
G. F. Cao - Inst High Energy Phys, Beijing, Peoples R China
W. R. Cen - Inst High Energy Phys, Beijing, Peoples R China
C. Chambers - Colorado State University
B. Cleveland - Laurentian University
M. Coon - University of Illinois Urbana-Champaign
A. Craycraft - Colorado State University
W. Cree - Carleton University
T. Daniels - University of North Carolina Wilmington
M. Danilov - Inst Theoret & Expt Phys, Moscow, Russia
S. J. Daugherty - Indiana University
J. Daughhetee - University of South Dakota
J. Davis - SLAC National Accelerator Laboratory
A. Der Mesrobian-Kabakian
R. DeVoe - Stanford University
J. Dilling - TRIUMF
A. Dolgolenko - Inst Theoret & Expt Phys, Moscow, Russia
M. J. Dolinski - Drexel University
W. Fairbank - Colorado State University
J. Farine - Laurentian University
S. Feyzbakhsh - University of Massachusetts Amherst
P. Fierlinger - Technical University of Munich
D. Fudenberg - Stanford University
R. Gornea - Carleton University
G. Gratta - Stanford University
C. Hall - Univ Maryland, Dept Phys, College Pk, MD 20742 USA
E. V. Hansen - Drexel University
D. Harris - Colorado State University
J. Hoessl - Friedrich-Alexander-Universität Erlangen-Nürnberg
P. Hufschmidt - Friedrich-Alexander-Universität Erlangen-Nürnberg
M. Hughes - University of Alabama
A. Iverson - Colorado State University
A. Jamil - Yale University
A. Johnson - SLAC National Accelerator Laboratory
A. Karelin - Inst Theoret & Expt Phys, Moscow, Russia
T. Koffas - Carleton University
S. Kravitz - Stanford University
R. Krucken - TRIUMF
A. Kuchenkov - Inst Theoret & Expt Phys, Moscow, Russia
K. S. Kumar - Stony Brook University
Y. Lan - TRIUMF
D. S. Leonard - Ibs
G. S. Li - Stanford University
S. Li - University of Illinois Urbana-Champaign
C. Licciardi - Laurentian University
Y. H. Lin
R. MacLellan - University of South Dakota
T. Michel - Friedrich-Alexander-Universität Erlangen-Nürnberg
B. Mong - SLAC National Accelerator Laboratory
D. Moore - Yale University
K. Murray - McGill University
O. Njoya - Stony Brook University
A. Odian - SLAC National Accelerator Laboratory
A. Piepke - University of Alabama
A. Pocar - University of Massachusetts Amherst
F. Retiere - TRIUMF
A. L. Robinson - Laurentian University
P. C. Rowson - SLAC National Accelerator Laboratory
S. Schmidt - Friedrich-Alexander-Universität Erlangen-Nürnberg
A. Schubert - Stanford University
D. Sinclair - Carleton University
A. K. Soma - University of Alabama
V. Stekhanov - Inst Theoret & Expt Phys, Moscow, Russia
M. Tarka - University of Massachusetts Amherst
J. Todd - Colorado State University
T. Tolba - Inst High Energy Phys, Beijing, Peoples R China
V. Veeraraghavan - University of Alabama
J. -L. Vuilleumier - University of Bern
M. Wagenpfeil - Friedrich-Alexander-Universität Erlangen-Nürnberg
A. Waite - SLAC National Accelerator Laboratory
J. Watkins - Carleton University
L. J. Wen - Inst High Energy Phys, Beijing, Peoples R China
U. Wichoski - Laurentian University
G. Wrede - Friedrich-Alexander-Universität Erlangen-Nürnberg
Q. Xia - Yale University
L. Yang - University of Illinois Urbana-Champaign
Y. -R. Yen - Drexel University
O. Ya Zeldovich - Inst Theoret & Expt Phys, Moscow, Russia
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Details: Journal of instrumentation, Vol.13(8), pp.P08023-P08023
Publisher: IOP Publishing Ltd
Number of pages: 33
Grant note: IBS in Korea NSERC in Canada; Natural Sciences and Engineering Research Council of Canada (NSERC) CAS in China Nvidia Corporation NSF in the U.S.; National Science Foundation (NSF) RFBR in Russia; Russian Foundation for Basic Research (RFBR) DOE; United States Department of Energy (DOE) Deutsche Forschungsgemeinschaft (DFG); German Research Foundation (DFG) SNF in Switzerland DE-AC02-05CH11231 / Office of Science of the U.S. Department of Energy; United States Department of Energy (DOE) DFG in Germany; German Research Foundation (DFG) ISTCP in China Erlangen Regional Computing Center (RRZE)
Identifiers: 9918712908331
Academic Unit: King Abdulaziz City for Science & Technology
Language: English
Resource Type: Journal article