Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground...
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| creator | Acciarri, R Adamowski, M Asquith, L Aushev, V Azfar, F Back, J J J Barranco Monarca Beacom, J F Bertolucci, S Bross, A Calin, M I Caro Terrazas Chattopadhyay, S M Chavarry Neyra Cheon, Y Chi, C Cho, K Chung, M Crisler, M Cudd, A Da Silva Peres, L Davies, G S I L De Icaza Astiz J R De Mello Neto Distefano, C Donon, Y Dunne, P Eisch, J Emberger, L Filthaut, F Furman, K Gallice, N Galymov, V Granger, P Grenard, J W Gu Gupta, A Hamacher-Baumann, P Harris, D A Hatzikoutelis, A Hill, T Hostert, M James, E Jesús-Valls, C Kosc, T Krennrich, F Kus, V A Laundrie Laurenti, G Leardini, S Lineros, R A Lockwitz, S Louis, W C X Lu Magill, S Maneira, J C Mascagna, V Mauri, N Montanari, C Mualem, L Muramatsu, H Nessi, M Norrick, A Ott, J Palamara, O Peeters, S J Pershey, D Piastra, F Pompa, F Qian, X Rafique, A Rigamonti, A Sacerdoti, S Safford, T Sala, P Shaw, T Smith, A Solovov, V Soto-Oton, J Stillwell, B J Suárez Sunción Surdo, A Susic, V Tayloe, R Ternes, C A Testera, G Thompson, J L Torti, M Tortorici, F Trilov, S Tsang, K Usher, T Uzunyan, S Wachala, T Wang, L Weber, A Wisniewski, W Worcester, E Yershov, N Zennamo, J |
| description | Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation. |
| doi_str_mv | 10.48550/arxiv.2203.17053 |
| format | Article |
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As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2203.17053</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Argon ; Artificial neural networks ; Charged particles ; Neural networks ; Neutrinos ; Particle physics ; Physics - High Energy Physics - Experiment ; Physics - Instrumentation and Detectors ; Radiation counters ; Sensors</subject><ispartof>arXiv.org, 2022-06</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,780,881,27904</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2203.17053$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1140/epjc/s10052-022-10791-2$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Acciarri, R</creatorcontrib><creatorcontrib>Adamowski, M</creatorcontrib><creatorcontrib>Asquith, L</creatorcontrib><creatorcontrib>Aushev, V</creatorcontrib><creatorcontrib>Azfar, F</creatorcontrib><creatorcontrib>Back, J 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A</creatorcontrib><creatorcontrib>Lockwitz, S</creatorcontrib><creatorcontrib>Louis, W C</creatorcontrib><creatorcontrib>X Lu</creatorcontrib><creatorcontrib>Magill, S</creatorcontrib><creatorcontrib>Maneira, J C</creatorcontrib><creatorcontrib>Mascagna, V</creatorcontrib><creatorcontrib>Mauri, N</creatorcontrib><creatorcontrib>Montanari, C</creatorcontrib><creatorcontrib>Mualem, L</creatorcontrib><creatorcontrib>Muramatsu, H</creatorcontrib><creatorcontrib>Nessi, M</creatorcontrib><creatorcontrib>Norrick, A</creatorcontrib><creatorcontrib>Ott, J</creatorcontrib><creatorcontrib>Palamara, O</creatorcontrib><creatorcontrib>Peeters, S J</creatorcontrib><creatorcontrib>Pershey, D</creatorcontrib><creatorcontrib>Piastra, F</creatorcontrib><creatorcontrib>Pompa, F</creatorcontrib><creatorcontrib>Qian, X</creatorcontrib><creatorcontrib>Rafique, A</creatorcontrib><creatorcontrib>Rigamonti, A</creatorcontrib><creatorcontrib>Sacerdoti, S</creatorcontrib><creatorcontrib>Safford, T</creatorcontrib><creatorcontrib>Sala, P</creatorcontrib><creatorcontrib>Shaw, T</creatorcontrib><creatorcontrib>Smith, A</creatorcontrib><creatorcontrib>Solovov, V</creatorcontrib><creatorcontrib>Soto-Oton, J</creatorcontrib><creatorcontrib>Stillwell, B</creatorcontrib><creatorcontrib>J Suárez Sunción</creatorcontrib><creatorcontrib>Surdo, A</creatorcontrib><creatorcontrib>Susic, V</creatorcontrib><creatorcontrib>Tayloe, R</creatorcontrib><creatorcontrib>Ternes, C A</creatorcontrib><creatorcontrib>Testera, G</creatorcontrib><creatorcontrib>Thompson, J L</creatorcontrib><creatorcontrib>Torti, M</creatorcontrib><creatorcontrib>Tortorici, F</creatorcontrib><creatorcontrib>Trilov, S</creatorcontrib><creatorcontrib>Tsang, K</creatorcontrib><creatorcontrib>Usher, T</creatorcontrib><creatorcontrib>Uzunyan, S</creatorcontrib><creatorcontrib>Wachala, T</creatorcontrib><creatorcontrib>Wang, L</creatorcontrib><creatorcontrib>Weber, A</creatorcontrib><creatorcontrib>Wisniewski, W</creatorcontrib><creatorcontrib>Worcester, E</creatorcontrib><creatorcontrib>Yershov, N</creatorcontrib><creatorcontrib>Zennamo, J</creatorcontrib><title>Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network</title><title>arXiv.org</title><description>Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.</description><subject>Algorithms</subject><subject>Argon</subject><subject>Artificial neural networks</subject><subject>Charged particles</subject><subject>Neural networks</subject><subject>Neutrinos</subject><subject>Particle physics</subject><subject>Physics - High Energy Physics - Experiment</subject><subject>Physics - Instrumentation and Detectors</subject><subject>Radiation counters</subject><subject>Sensors</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotkEtLAzEYRYMgWGp_gCsDrlO_vGaSpdT6gKKF1vWQ6WRq2jEZkxlr_719uLpwuRwuB6EbCmOhpIR7E3_dz5gx4GOag-QXaMA4p0QJxq7QKKUNALAsZ1LyAXIL25poOhc8DjXuolltCTa-wukz7GwkjdtabL2N6z2ubBuS6xJ2Hs9j6MLjx9uULOa4T86vscGr4H9C0x9ppsHe9vEU3S7E7TW6rE2T7Og_h2j5NF1OXsjs_fl18jAjRjJGTKnzmoMSUuhSqFpwkdW1hhJ0KXWmV1qLjIKtOD30zHBhqkzTEhRwRZXlQ3R7xp48FG10Xybui6OP4uTjsLg7L9oYvnubumIT-ng4nAqWiQyoYjnjf7U4Ys8</recordid><startdate>20220630</startdate><enddate>20220630</enddate><creator>Acciarri, R</creator><creator>Adamowski, M</creator><creator>Asquith, L</creator><creator>Aushev, V</creator><creator>Azfar, F</creator><creator>Back, J J</creator><creator>J Barranco Monarca</creator><creator>Beacom, J F</creator><creator>Bertolucci, S</creator><creator>Bross, A</creator><creator>Calin, M</creator><creator>I Caro Terrazas</creator><creator>Chattopadhyay, S</creator><creator>M Chavarry Neyra</creator><creator>Cheon, Y</creator><creator>Chi, C</creator><creator>Cho, K</creator><creator>Chung, M</creator><creator>Crisler, M</creator><creator>Cudd, A</creator><creator>Da Silva Peres, L</creator><creator>Davies, G S</creator><creator>I L De Icaza Astiz</creator><creator>J R De Mello Neto</creator><creator>Distefano, C</creator><creator>Donon, Y</creator><creator>Dunne, P</creator><creator>Eisch, J</creator><creator>Emberger, L</creator><creator>Filthaut, F</creator><creator>Furman, K</creator><creator>Gallice, N</creator><creator>Galymov, V</creator><creator>Granger, P</creator><creator>Grenard, J</creator><creator>W Gu</creator><creator>Gupta, A</creator><creator>Hamacher-Baumann, P</creator><creator>Harris, D 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arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20220630</creationdate><title>Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network</title><author>Acciarri, R ; Adamowski, M ; Asquith, L ; Aushev, V ; Azfar, F ; Back, J J ; J Barranco Monarca ; Beacom, J F ; Bertolucci, S ; Bross, A ; Calin, M ; I Caro Terrazas ; Chattopadhyay, S ; M Chavarry Neyra ; Cheon, Y ; Chi, C ; Cho, K ; Chung, M ; Crisler, M ; Cudd, A ; Da Silva Peres, L ; Davies, G S ; I L De Icaza Astiz ; J R De Mello Neto ; Distefano, C ; Donon, Y ; Dunne, P ; Eisch, J ; Emberger, L ; Filthaut, F ; Furman, K ; Gallice, N ; Galymov, V ; Granger, P ; Grenard, J ; W Gu ; Gupta, A ; Hamacher-Baumann, P ; Harris, D A ; Hatzikoutelis, A ; Hill, T ; Hostert, M ; James, E ; Jesús-Valls, C ; Kosc, T ; Krennrich, F ; Kus, V ; A Laundrie ; Laurenti, G ; Leardini, S ; Lineros, R A ; Lockwitz, S ; Louis, W C ; X Lu ; Magill, S ; Maneira, J C ; Mascagna, V ; Mauri, N ; Montanari, C ; Mualem, L ; Muramatsu, H ; Nessi, M ; Norrick, A ; Ott, J ; Palamara, O ; Peeters, S J ; Pershey, D ; Piastra, F ; Pompa, F ; Qian, X ; Rafique, A ; Rigamonti, A ; Sacerdoti, S ; Safford, T ; Sala, P ; Shaw, T ; Smith, A ; Solovov, V ; Soto-Oton, J ; Stillwell, B ; J Suárez Sunción ; Surdo, A ; Susic, V ; Tayloe, R ; Ternes, C A ; Testera, G ; Thompson, J L ; Torti, M ; Tortorici, F ; Trilov, S ; Tsang, K ; Usher, T ; Uzunyan, S ; Wachala, T ; Wang, L ; Weber, A ; Wisniewski, W ; Worcester, E ; Yershov, N ; Zennamo, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a522-ab97f3084549b48f4346ff90b09b5969c994610ed316ff2a34ad691b0803818e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Argon</topic><topic>Artificial neural networks</topic><topic>Charged particles</topic><topic>Neural networks</topic><topic>Neutrinos</topic><topic>Particle physics</topic><topic>Physics - High Energy Physics - Experiment</topic><topic>Physics - Instrumentation and Detectors</topic><topic>Radiation counters</topic><topic>Sensors</topic><toplevel>online_resources</toplevel><creatorcontrib>Acciarri, R</creatorcontrib><creatorcontrib>Adamowski, M</creatorcontrib><creatorcontrib>Asquith, L</creatorcontrib><creatorcontrib>Aushev, V</creatorcontrib><creatorcontrib>Azfar, F</creatorcontrib><creatorcontrib>Back, J J</creatorcontrib><creatorcontrib>J Barranco Monarca</creatorcontrib><creatorcontrib>Beacom, J F</creatorcontrib><creatorcontrib>Bertolucci, S</creatorcontrib><creatorcontrib>Bross, A</creatorcontrib><creatorcontrib>Calin, M</creatorcontrib><creatorcontrib>I Caro Terrazas</creatorcontrib><creatorcontrib>Chattopadhyay, S</creatorcontrib><creatorcontrib>M Chavarry Neyra</creatorcontrib><creatorcontrib>Cheon, Y</creatorcontrib><creatorcontrib>Chi, C</creatorcontrib><creatorcontrib>Cho, K</creatorcontrib><creatorcontrib>Chung, M</creatorcontrib><creatorcontrib>Crisler, M</creatorcontrib><creatorcontrib>Cudd, A</creatorcontrib><creatorcontrib>Da Silva Peres, L</creatorcontrib><creatorcontrib>Davies, G S</creatorcontrib><creatorcontrib>I L De Icaza Astiz</creatorcontrib><creatorcontrib>J R De Mello Neto</creatorcontrib><creatorcontrib>Distefano, C</creatorcontrib><creatorcontrib>Donon, Y</creatorcontrib><creatorcontrib>Dunne, P</creatorcontrib><creatorcontrib>Eisch, J</creatorcontrib><creatorcontrib>Emberger, L</creatorcontrib><creatorcontrib>Filthaut, F</creatorcontrib><creatorcontrib>Furman, K</creatorcontrib><creatorcontrib>Gallice, N</creatorcontrib><creatorcontrib>Galymov, V</creatorcontrib><creatorcontrib>Granger, P</creatorcontrib><creatorcontrib>Grenard, J</creatorcontrib><creatorcontrib>W Gu</creatorcontrib><creatorcontrib>Gupta, A</creatorcontrib><creatorcontrib>Hamacher-Baumann, P</creatorcontrib><creatorcontrib>Harris, D A</creatorcontrib><creatorcontrib>Hatzikoutelis, A</creatorcontrib><creatorcontrib>Hill, T</creatorcontrib><creatorcontrib>Hostert, M</creatorcontrib><creatorcontrib>James, E</creatorcontrib><creatorcontrib>Jesús-Valls, C</creatorcontrib><creatorcontrib>Kosc, T</creatorcontrib><creatorcontrib>Krennrich, F</creatorcontrib><creatorcontrib>Kus, V</creatorcontrib><creatorcontrib>A Laundrie</creatorcontrib><creatorcontrib>Laurenti, G</creatorcontrib><creatorcontrib>Leardini, S</creatorcontrib><creatorcontrib>Lineros, R A</creatorcontrib><creatorcontrib>Lockwitz, S</creatorcontrib><creatorcontrib>Louis, W C</creatorcontrib><creatorcontrib>X Lu</creatorcontrib><creatorcontrib>Magill, S</creatorcontrib><creatorcontrib>Maneira, J C</creatorcontrib><creatorcontrib>Mascagna, V</creatorcontrib><creatorcontrib>Mauri, N</creatorcontrib><creatorcontrib>Montanari, C</creatorcontrib><creatorcontrib>Mualem, L</creatorcontrib><creatorcontrib>Muramatsu, H</creatorcontrib><creatorcontrib>Nessi, M</creatorcontrib><creatorcontrib>Norrick, A</creatorcontrib><creatorcontrib>Ott, J</creatorcontrib><creatorcontrib>Palamara, O</creatorcontrib><creatorcontrib>Peeters, S J</creatorcontrib><creatorcontrib>Pershey, D</creatorcontrib><creatorcontrib>Piastra, F</creatorcontrib><creatorcontrib>Pompa, F</creatorcontrib><creatorcontrib>Qian, X</creatorcontrib><creatorcontrib>Rafique, A</creatorcontrib><creatorcontrib>Rigamonti, A</creatorcontrib><creatorcontrib>Sacerdoti, S</creatorcontrib><creatorcontrib>Safford, T</creatorcontrib><creatorcontrib>Sala, P</creatorcontrib><creatorcontrib>Shaw, T</creatorcontrib><creatorcontrib>Smith, A</creatorcontrib><creatorcontrib>Solovov, V</creatorcontrib><creatorcontrib>Soto-Oton, J</creatorcontrib><creatorcontrib>Stillwell, B</creatorcontrib><creatorcontrib>J Suárez Sunción</creatorcontrib><creatorcontrib>Surdo, A</creatorcontrib><creatorcontrib>Susic, V</creatorcontrib><creatorcontrib>Tayloe, R</creatorcontrib><creatorcontrib>Ternes, C A</creatorcontrib><creatorcontrib>Testera, G</creatorcontrib><creatorcontrib>Thompson, J L</creatorcontrib><creatorcontrib>Torti, M</creatorcontrib><creatorcontrib>Tortorici, F</creatorcontrib><creatorcontrib>Trilov, S</creatorcontrib><creatorcontrib>Tsang, K</creatorcontrib><creatorcontrib>Usher, T</creatorcontrib><creatorcontrib>Uzunyan, S</creatorcontrib><creatorcontrib>Wachala, T</creatorcontrib><creatorcontrib>Wang, L</creatorcontrib><creatorcontrib>Weber, A</creatorcontrib><creatorcontrib>Wisniewski, W</creatorcontrib><creatorcontrib>Worcester, E</creatorcontrib><creatorcontrib>Yershov, N</creatorcontrib><creatorcontrib>Zennamo, J</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Acciarri, R</au><au>Adamowski, M</au><au>Asquith, L</au><au>Aushev, V</au><au>Azfar, F</au><au>Back, J J</au><au>J Barranco Monarca</au><au>Beacom, J F</au><au>Bertolucci, S</au><au>Bross, A</au><au>Calin, M</au><au>I Caro Terrazas</au><au>Chattopadhyay, S</au><au>M Chavarry Neyra</au><au>Cheon, Y</au><au>Chi, C</au><au>Cho, K</au><au>Chung, M</au><au>Crisler, M</au><au>Cudd, A</au><au>Da Silva Peres, L</au><au>Davies, G S</au><au>I L De Icaza Astiz</au><au>J R De Mello Neto</au><au>Distefano, C</au><au>Donon, Y</au><au>Dunne, P</au><au>Eisch, J</au><au>Emberger, L</au><au>Filthaut, F</au><au>Furman, K</au><au>Gallice, N</au><au>Galymov, V</au><au>Granger, P</au><au>Grenard, J</au><au>W Gu</au><au>Gupta, A</au><au>Hamacher-Baumann, P</au><au>Harris, D A</au><au>Hatzikoutelis, A</au><au>Hill, T</au><au>Hostert, M</au><au>James, E</au><au>Jesús-Valls, C</au><au>Kosc, T</au><au>Krennrich, F</au><au>Kus, V</au><au>A Laundrie</au><au>Laurenti, G</au><au>Leardini, S</au><au>Lineros, R A</au><au>Lockwitz, S</au><au>Louis, W C</au><au>X Lu</au><au>Magill, S</au><au>Maneira, J C</au><au>Mascagna, V</au><au>Mauri, N</au><au>Montanari, C</au><au>Mualem, L</au><au>Muramatsu, H</au><au>Nessi, M</au><au>Norrick, A</au><au>Ott, J</au><au>Palamara, O</au><au>Peeters, S J</au><au>Pershey, D</au><au>Piastra, F</au><au>Pompa, F</au><au>Qian, X</au><au>Rafique, A</au><au>Rigamonti, A</au><au>Sacerdoti, S</au><au>Safford, T</au><au>Sala, P</au><au>Shaw, T</au><au>Smith, A</au><au>Solovov, V</au><au>Soto-Oton, J</au><au>Stillwell, B</au><au>J Suárez Sunción</au><au>Surdo, A</au><au>Susic, V</au><au>Tayloe, R</au><au>Ternes, C A</au><au>Testera, G</au><au>Thompson, J L</au><au>Torti, M</au><au>Tortorici, F</au><au>Trilov, S</au><au>Tsang, K</au><au>Usher, T</au><au>Uzunyan, S</au><au>Wachala, T</au><au>Wang, L</au><au>Weber, A</au><au>Wisniewski, W</au><au>Worcester, E</au><au>Yershov, N</au><au>Zennamo, J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network</atitle><jtitle>arXiv.org</jtitle><date>2022-06-30</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2203.17053</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2022-06 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_arxiv_primary_2203_17053 |
| source | arXiv.org; Free E- Journals |
| subjects | Algorithms Argon Artificial neural networks Charged particles Neural networks Neutrinos Particle physics Physics - High Energy Physics - Experiment Physics - Instrumentation and Detectors Radiation counters Sensors |
| title | Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network |
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