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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:
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Publication Date:
Research Org.:
SLAC National Accelerator Lab., 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. 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., and Zeldovich, O. Ya. Wed . "Deep neural networks for energy and position reconstruction in EXO-200". United States. doi: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 = {2018},
month = {8}
}

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

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