Deep neural networks for energy and position reconstruction in EXO-200
Abstract
Here, 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.
- Authors:
- more »
- Publication Date:
- Research Org.:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- Contributing Org.:
- EXO-200 Collaboration
- OSTI Identifier:
- 1475444
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Instrumentation
- Additional Journal Information:
- Journal Volume: 13; Journal Issue: 08; Journal ID: ISSN 1748-0221
- Publisher:
- Institute of Physics (IOP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Analysis and statistical methods; Double-beta decay detectors; Pattern recognition, cluster finding, calibration and fitting methods; Time projection chambers
Citation Formats
Delaquis, S., Jewell, M. J., Ostrovskiy, I., Weber, M., Ziegler, T., Dalmasson, J., Kaufman, L. J., Richards, T., Albert, J. B., Anton, G., Badhrees, I., Barbeau, P. S., Bayerlein, R., Beck, D., Belov, V., Breidenbach, M., Brunner, T., Cao, G. F., Cen, W. R., Chambers, C., Cleveland, B., Coon, M., Craycraft, A., Cree, W., Daniels, T., Danilov, M., Daugherty, S. J., Daughhetee, J., Davis, J., Mesrobian-Kabakian, A. Der, DeVoe, R., Dilling, J., Dolgolenko, A., Dolinski, M. J., Jr., W. Fairbank, Farine, J., Feyzbakhsh, S., Fierlinger, P., Fudenberg, D., Gornea, R., Gratta, G., Hall, C., Hansen, E. V., 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, K. S., Lan, Y., Leonard, D. S., Li, G. S., Li, S., Licciardi, C., Lin, Y. H., MacLellan, R., Michel, T., Mong, B., Moore, D., Murray, K., Njoya, O., Odian, A., Piepke, A., Pocar, A., Retière, F., Robinson, A. L., Rowson, P. C., Schmidt, S., Schubert, A., Sinclair, D., Soma, A. K., Stekhanov, V., Tarka, M., Todd, J., Tolba, T., Veeraraghavan, V., Vuilleumier, J. -L., Wagenpfeil, M., Waite, A., Watkins, J., Wen, L. J., Wichoski, U., Wrede, G., Xia, Q., Yang, L., Yen, Y. -R., and Zeldovich, O. Ya. Deep neural networks for energy and position reconstruction in EXO-200. United States: N. p., 2018.
Web. doi:10.1088/1748-0221/13/08/p08023.
Delaquis, S., Jewell, M. J., Ostrovskiy, I., Weber, M., Ziegler, T., Dalmasson, J., Kaufman, L. J., Richards, T., Albert, J. B., Anton, G., Badhrees, I., Barbeau, P. S., Bayerlein, R., Beck, D., Belov, V., Breidenbach, M., Brunner, T., Cao, G. F., Cen, W. R., Chambers, C., Cleveland, B., Coon, M., Craycraft, A., Cree, W., Daniels, T., Danilov, M., Daugherty, S. J., Daughhetee, J., Davis, J., Mesrobian-Kabakian, A. Der, DeVoe, R., Dilling, J., Dolgolenko, A., Dolinski, M. J., Jr., W. Fairbank, Farine, J., Feyzbakhsh, S., Fierlinger, P., Fudenberg, D., Gornea, R., Gratta, G., Hall, C., Hansen, E. V., 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, K. S., Lan, Y., Leonard, D. S., Li, G. S., Li, S., Licciardi, C., Lin, Y. H., MacLellan, R., Michel, T., Mong, B., Moore, D., Murray, K., Njoya, O., Odian, A., Piepke, A., Pocar, A., Retière, F., Robinson, A. L., Rowson, P. C., Schmidt, S., Schubert, A., Sinclair, D., Soma, A. K., Stekhanov, V., Tarka, M., Todd, J., Tolba, T., Veeraraghavan, V., Vuilleumier, J. -L., Wagenpfeil, M., Waite, A., Watkins, J., Wen, L. J., Wichoski, U., Wrede, G., Xia, Q., Yang, L., Yen, Y. -R., & Zeldovich, O. Ya. Deep neural networks for energy and position reconstruction in EXO-200. United States. https://doi.org/10.1088/1748-0221/13/08/p08023
Delaquis, S., Jewell, M. J., Ostrovskiy, I., Weber, M., Ziegler, T., Dalmasson, J., Kaufman, L. J., Richards, T., Albert, J. B., Anton, G., Badhrees, I., Barbeau, P. S., Bayerlein, R., Beck, D., Belov, V., Breidenbach, M., Brunner, T., Cao, G. F., Cen, W. R., Chambers, C., Cleveland, B., Coon, M., Craycraft, A., Cree, W., Daniels, T., Danilov, M., Daugherty, S. J., Daughhetee, J., Davis, J., Mesrobian-Kabakian, A. Der, DeVoe, R., Dilling, J., Dolgolenko, A., Dolinski, M. J., Jr., W. Fairbank, Farine, J., Feyzbakhsh, S., Fierlinger, P., Fudenberg, D., Gornea, R., Gratta, G., Hall, C., Hansen, E. V., 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, K. S., Lan, Y., Leonard, D. S., Li, G. S., Li, S., Licciardi, C., Lin, Y. H., MacLellan, R., Michel, T., Mong, B., Moore, D., Murray, K., Njoya, O., Odian, A., Piepke, A., Pocar, A., Retière, F., Robinson, A. L., Rowson, P. C., Schmidt, S., Schubert, A., Sinclair, D., Soma, A. K., Stekhanov, V., Tarka, M., Todd, J., Tolba, T., Veeraraghavan, V., Vuilleumier, J. -L., Wagenpfeil, M., Waite, A., Watkins, J., Wen, L. J., Wichoski, U., Wrede, G., Xia, Q., Yang, L., Yen, Y. -R., and Zeldovich, O. Ya. Wed .
"Deep neural networks for energy and position reconstruction in EXO-200". United States. https://doi.org/10.1088/1748-0221/13/08/p08023. https://www.osti.gov/servlets/purl/1475444.
@article{osti_1475444,
title = {Deep neural networks for energy and position reconstruction in EXO-200},
author = {Delaquis, S. and Jewell, M. J. and Ostrovskiy, I. and Weber, M. and Ziegler, T. and Dalmasson, J. and Kaufman, L. J. and Richards, T. and Albert, J. B. and Anton, G. and Badhrees, I. and Barbeau, P. S. and Bayerlein, R. and Beck, D. and Belov, V. and Breidenbach, M. and Brunner, T. and Cao, G. F. and Cen, W. R. and Chambers, C. and Cleveland, B. and Coon, M. and Craycraft, A. and Cree, W. and Daniels, T. and Danilov, M. and Daugherty, S. J. and Daughhetee, J. and Davis, J. and Mesrobian-Kabakian, A. Der and DeVoe, R. and Dilling, J. and Dolgolenko, A. and Dolinski, M. J. and Jr., W. Fairbank and Farine, J. and Feyzbakhsh, S. and Fierlinger, P. and Fudenberg, D. and Gornea, R. and Gratta, G. and Hall, C. and Hansen, E. V. and Harris, D. and Hoessl, J. and Hufschmidt, P. and Hughes, M. and Iverson, A. and Jamil, A. and Johnson, A. and Karelin, A. and Koffas, T. and Kravitz, S. and Krücken, R. and Kuchenkov, A. and Kumar, K. S. and Lan, Y. and Leonard, D. S. and Li, G. S. and Li, S. and Licciardi, C. and Lin, Y. H. and MacLellan, R. and Michel, T. and Mong, B. and Moore, D. and Murray, K. and Njoya, O. and Odian, A. and Piepke, A. and Pocar, A. and Retière, F. and Robinson, A. L. and Rowson, P. C. and Schmidt, S. and Schubert, A. and Sinclair, D. and Soma, A. K. and Stekhanov, V. and Tarka, M. and Todd, J. and Tolba, T. and Veeraraghavan, V. and Vuilleumier, J. -L. and Wagenpfeil, M. and Waite, A. and Watkins, J. and Wen, L. J. and Wichoski, U. and Wrede, G. and Xia, Q. and Yang, L. and Yen, Y. -R. and Zeldovich, O. Ya.},
abstractNote = {Here, 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.},
doi = {10.1088/1748-0221/13/08/p08023},
journal = {Journal of Instrumentation},
number = 08,
volume = 13,
place = {United States},
year = {Wed Aug 29 00:00:00 EDT 2018},
month = {Wed Aug 29 00:00:00 EDT 2018}
}
Web of Science
Figures / Tables:
Works referenced in this record:
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
journal, March 2017
- Acciarri, R.; Adams, C.; An, R.
- Journal of Instrumentation, Vol. 12, Issue 03
Searching for exotic particles in high-energy physics with deep learning
journal, July 2014
- Baldi, P.; Sadowski, P.; Whiteson, D.
- Nature Communications, Vol. 5, Issue 1
A convolutional neural network neutrino event classifier
journal, September 2016
- Aurisano, A.; Radovic, A.; Rocco, D.
- Journal of Instrumentation, Vol. 11, Issue 09
Background rejection in NEXT using deep neural networks
journal, January 2017
- Renner, J.; Farbin, A.; Vidal, J. Muñoz
- Journal of Instrumentation, Vol. 12, Issue 01
Jet flavor classification in high-energy physics with deep neural networks
journal, December 2016
- Guest, Daniel; Collado, Julian; Baldi, Pierre
- Physical Review D, Vol. 94, Issue 11
The EXO-200 detector, part I: detector design and construction
journal, May 2012
- Auger, M.; Auty, D. J.; Barbeau, P. S.
- Journal of Instrumentation, Vol. 7, Issue 05
Errata: “Search for neutrinoless double beta decay”
journal, July 2016
- Ostrovskiy, I.; O’Sullivan, K.
- Modern Physics Letters A, Vol. 31, Issue 23
Search for Neutrinoless Double-Beta Decay with the Upgraded EXO-200 Detector
journal, February 2018
- Albert, J. B.; Anton, G.; Badhrees, I.
- Physical Review Letters, Vol. 120, Issue 7
Improved measurement of the half-life of Xe with the EXO-200 detector
journal, January 2014
- Albert, J. B.; Auger, M.; Auty, D. J.
- Physical Review C, Vol. 89, Issue 1
An optimal energy estimator to reduce correlated noise for the EXO-200 light readout
journal, July 2016
- Davis, C. G.; Hall, C.; Albert, J. B.
- Journal of Instrumentation, Vol. 11, Issue 07
Correlated fluctuations between luminescence and ionization in liquid xenon
journal, August 2003
- Conti, E.; DeVoe, R.; Gratta, G.
- Physical Review B, Vol. 68, Issue 5
Geant4 developments and applications
journal, February 2006
- Allison, J.; Amako, K.; Apostolakis, J.
- IEEE Transactions on Nuclear Science, Vol. 53, Issue 1
Geant4—a simulation toolkit
journal, July 2003
- Agostinelli, S.; Allison, J.; Amako, K.
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3
Measurement of the drift velocity and transverse diffusion of electrons in liquid xenon with the EXO-200 detector
journal, February 2017
- Albert, J. B.; Barbeau, P. S.; Beck, D.
- Physical Review C, Vol. 95, Issue 2
Currents to Conductors Induced by a Moving Point Charge
journal, October 1938
- Shockley, W.
- Journal of Applied Physics, Vol. 9, Issue 10
Currents Induced by Electron Motion
journal, September 1939
- Ramo, S.
- Proceedings of the IRE, Vol. 27, Issue 9
Search for Neutrinoless Double-Beta Decay in with EXO-200
journal, July 2012
- Auger, M.; Auty, D. J.; Barbeau, P. S.
- Physical Review Letters, Vol. 109, Issue 3
On Estimation of a Probability Density Function and Mode
journal, September 1962
- Parzen, Emanuel
- The Annals of Mathematical Statistics, Vol. 33, Issue 3
Currents to Conductors Induced by a Moving Point Charge
journal, October 1938
- Shockley, W.
- Journal of Applied Physics, Vol. 9, Issue 10
Search for neutrinoless double beta decay
journal, June 2016
- Ostrovskiy, Igor; O’Sullivan, Kevin
- Modern Physics Letters A, Vol. 31, Issue 18
Works referencing / citing this record:
Search for Neutrinoless Double- β Decay with the Complete EXO-200 Dataset
journal, October 2019
- Anton, G.; Badhrees, I.; Barbeau, P. S.
- Physical Review Letters, Vol. 123, Issue 16
Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset
text, January 2019
- Anton, G.; Badhrees, I.; Barbeau, P. S.
- arXiv
Figures / Tables found in this record: