Deep neural networks for energy and position reconstruction in EXO-200
| Publication Date: |
2018-08-30
|
|---|---|
| Publisher: |
Institute of Physics Publishing (IOP)
|
| Electronic ISSN: |
1748-0221
|
| Topics: |
Physics
|
| Published by: |
| _version_ | 1836399039357124608 |
|---|---|
| autor | 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich |
| beschreibung | 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. |
| citation_standardnr | 6325494 |
| datenlieferant | ipn_articles |
| feed_id | 66992 |
| feed_publisher | Institute of Physics Publishing (IOP) |
| feed_publisher_url | http://www.iop.org/ |
| insertion_date | 2018-08-30 |
| journaleissn | 1748-0221 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Institute of Physics Publishing (IOP) |
| quelle | Journal of Instrumentation |
| relation | http://iopscience.iop.org/1748-0221/13/08/P08023 |
| search_space | articles |
| shingle_author_1 | 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich |
| shingle_author_2 | 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich |
| shingle_author_3 | 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich |
| shingle_author_4 | 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich |
| shingle_catch_all_1 | Deep neural networks for energy and position reconstruction in EXO-200 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. 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich Institute of Physics Publishing (IOP) 1748-0221 17480221 |
| shingle_catch_all_2 | Deep neural networks for energy and position reconstruction in EXO-200 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. 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich Institute of Physics Publishing (IOP) 1748-0221 17480221 |
| shingle_catch_all_3 | Deep neural networks for energy and position reconstruction in EXO-200 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. 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich Institute of Physics Publishing (IOP) 1748-0221 17480221 |
| shingle_catch_all_4 | Deep neural networks for energy and position reconstruction in EXO-200 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. 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. De; Voe, J. Dilling, A. Dolgolenko, M.J. Dolinski, W. Fairbank Jr., 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. Krücken, A. Kuchenkov, K.S. Kumar, Y. Lan, D.S. Leonard, G.S. Li, S. Li, C. Licciardi, Y.H. Lin, R. Mac; Lellan, T. Michel, B. Mong, D. Moore, K. Murray, O. Njoya, A. Odian, A. Piepke, A. Pocar, F. Retière, 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 and O.Ya. Zeldovich Institute of Physics Publishing (IOP) 1748-0221 17480221 |
| shingle_title_1 | Deep neural networks for energy and position reconstruction in EXO-200 |
| shingle_title_2 | Deep neural networks for energy and position reconstruction in EXO-200 |
| shingle_title_3 | Deep neural networks for energy and position reconstruction in EXO-200 |
| shingle_title_4 | Deep neural networks for energy and position reconstruction in EXO-200 |
| timestamp | 2025-06-30T23:36:38.457Z |
| titel | Deep neural networks for energy and position reconstruction in EXO-200 |
| titel_suche | Deep neural networks for energy and position reconstruction in EXO-200 |
| topic | U |
| uid | ipn_articles_6325494 |