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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