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

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.

doi:10.1088/1748-0221/13/08/p08023

Title: 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:: 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. more »« less

Publication Date:: Wed Aug 29 00:00:00 EDT 2018

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.

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                    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

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                    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.

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@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}

}

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https://doi.org/10.1088/1748-0221/13/08/p08023

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Cited by: 20 works

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Figure 1: Calibration source positions around the detector vessel. Details in the text.

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Works referencing / citing this record:

Search for Neutrinoless Double- β Decay with the Complete EXO-200 Dataset
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Anton, G.; Badhrees, I.; Barbeau, P. S.
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Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset
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Anton, G.; Badhrees, I.; Barbeau, P. S.
arXiv
DOI: 10.48550/arxiv.1906.02723

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

Abstract

Citation Formats

Figures / Tables:

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Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber journal, March 2017

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Search for Neutrinoless Double-Beta Decay with the Upgraded EXO-200 Detector journal, February 2018

Improved measurement of the 2 ν β β half-life of 136 Xe with the EXO-200 detector journal, January 2014

An optimal energy estimator to reduce correlated noise for the EXO-200 light readout journal, July 2016

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Currents Induced by Electron Motion journal, September 1939

Search for Neutrinoless Double-Beta Decay in Xe 136 with EXO-200 journal, July 2012

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Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset text, January 2019

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