Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. Th...
| Egile Nagusiak: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formatua: | Journal article |
| Argitaratua: |
American Physical Society
2019
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| _version_ | 1826295621200379904 |
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| author | Adams, C Alrashed, M An, R Anthony, J Asaadi, J Ashkenazi, A Auger, M Balasubramanian, S Baller, B Barnes, C Barr, G Bass, M Bay, F Bhat, A Bhattacharya, K Bishai, M Blake, A Bolton, T Camilleri, L Caratelli, D Terrazas, IC Carr, R Fernandez, RC Cavanna, F Cerati, G Chen, Y Church, E Cianci, D Cohen, EO Collin, GH Conrad, JM Convery, M Cooper-Troendle, L Crespo-Anadon, JI Del Tutto, M Devitt, D Diaz, A Duffy, K Dytman, S Eberly, B Ereditato, A Sanchez, LE Esquivel, J Evans, JJ Fadeeva, AA Fitzpatrick, RS Fleming, BT Franco, D Furmanski, AP Garcia-Gamez, D Genty, V Goeldi, D Gollapinni, S Goodwin, O Gramellini, E Greenlee, H Grosso, R Guenette, R Guzowski, P Hackenburg, A Hamilton, P Hen, O Hewes, J Hill, C Horton-Smith, GA Hourlier, A Huang, E-C James, C De Vries, JJ Ji, X Jiang, L Johnson, RA Joshi, J Jostlein, H Jwa, Y-J Karagiorgi, G Ketchum, W Kirby, B Kirby, M Kobilarcik, T Kreslo, I Lepetic, I Li, Y Lister, A Littlejohn, BR Lockwitz, S Lorca, D Louis, WC Luethi, M Lundberg, B Luo, X Marchionni, A Marcocci, S Mariani, C Marshall, J Martin-Albo, J Caicedo, DAM Mastbaum, A Meddage, V Mettler, T Mistry, K Mogan, A Moon, J Mooney, M Moore, CD Mousseau, J Murphy, M Murrells, R Naples, D Nienaber, P Nowak, J Palamara, O Pandey, V Paolone, V Papadopoulou, A Papavassiliou, V Pate, SF Pavlovic, Z Piasetzky, E Porzio, D Pulliam, G Qian, X Raaf, JL Rafique, A Ren, L Rochester, L Ross-Lonergan, M Von Rohr, CR Russell, B Scanavini, G Schmitz, DW Schukraft, A Seligman, W Shaevitz, MH Sharankova, R Sinclair, J Smith, A Snider, EL Soderberg, M Soldner-Rembold, S Soleti, SR Spentzouris, P Spitz, J St John, J Strauss, T Sutton, K Sword-Fehlberg, S Szelc, AM Tagg, N Tang, W Terao, K Thomson, M Thornton, RT Toups, M Tsai, Y-T Tufanli, S Usher, T Van De Pontseele, W Van De Water, RG Viren, B Weber, M Wei, H Wickremasinghe, DA Wierman, K Williams, Z Wolbers, S Wongjirad, T Woodruff, K Yang, T Yarbrough, G Yates, LE Zeller, GP Zennamo, J Zhang, C Collaboration, M |
| author_facet | Adams, C Alrashed, M An, R Anthony, J Asaadi, J Ashkenazi, A Auger, M Balasubramanian, S Baller, B Barnes, C Barr, G Bass, M Bay, F Bhat, A Bhattacharya, K Bishai, M Blake, A Bolton, T Camilleri, L Caratelli, D Terrazas, IC Carr, R Fernandez, RC Cavanna, F Cerati, G Chen, Y Church, E Cianci, D Cohen, EO Collin, GH Conrad, JM Convery, M Cooper-Troendle, L Crespo-Anadon, JI Del Tutto, M Devitt, D Diaz, A Duffy, K Dytman, S Eberly, B Ereditato, A Sanchez, LE Esquivel, J Evans, JJ Fadeeva, AA Fitzpatrick, RS Fleming, BT Franco, D Furmanski, AP Garcia-Gamez, D Genty, V Goeldi, D Gollapinni, S Goodwin, O Gramellini, E Greenlee, H Grosso, R Guenette, R Guzowski, P Hackenburg, A Hamilton, P Hen, O Hewes, J Hill, C Horton-Smith, GA Hourlier, A Huang, E-C James, C De Vries, JJ Ji, X Jiang, L Johnson, RA Joshi, J Jostlein, H Jwa, Y-J Karagiorgi, G Ketchum, W Kirby, B Kirby, M Kobilarcik, T Kreslo, I Lepetic, I Li, Y Lister, A Littlejohn, BR Lockwitz, S Lorca, D Louis, WC Luethi, M Lundberg, B Luo, X Marchionni, A Marcocci, S Mariani, C Marshall, J Martin-Albo, J Caicedo, DAM Mastbaum, A Meddage, V Mettler, T Mistry, K Mogan, A Moon, J Mooney, M Moore, CD Mousseau, J Murphy, M Murrells, R Naples, D Nienaber, P Nowak, J Palamara, O Pandey, V Paolone, V Papadopoulou, A Papavassiliou, V Pate, SF Pavlovic, Z Piasetzky, E Porzio, D Pulliam, G Qian, X Raaf, JL Rafique, A Ren, L Rochester, L Ross-Lonergan, M Von Rohr, CR Russell, B Scanavini, G Schmitz, DW Schukraft, A Seligman, W Shaevitz, MH Sharankova, R Sinclair, J Smith, A Snider, EL Soderberg, M Soldner-Rembold, S Soleti, SR Spentzouris, P Spitz, J St John, J Strauss, T Sutton, K Sword-Fehlberg, S Szelc, AM Tagg, N Tang, W Terao, K Thomson, M Thornton, RT Toups, M Tsai, Y-T Tufanli, S Usher, T Van De Pontseele, W Van De Water, RG Viren, B Weber, M Wei, H Wickremasinghe, DA Wierman, K Williams, Z Wolbers, S Wongjirad, T Woodruff, K Yang, T Yarbrough, G Yates, LE Zeller, GP Zennamo, J Zhang, C Collaboration, M |
| author_sort | Adams, C |
| collection | OXFORD |
| description | We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples. |
| first_indexed | 2024-03-07T04:03:51Z |
| format | Journal article |
| id | oxford-uuid:c57cd89d-1ace-4036-ab29-12f34cff23c7 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:03:51Z |
| publishDate | 2019 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | oxford-uuid:c57cd89d-1ace-4036-ab29-12f34cff23c72022-03-27T06:31:47ZDeep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamberJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c57cd89d-1ace-4036-ab29-12f34cff23c7Symplectic Elements at OxfordAmerican Physical Society2019Adams, CAlrashed, MAn, RAnthony, JAsaadi, JAshkenazi, AAuger, MBalasubramanian, SBaller, BBarnes, CBarr, GBass, MBay, FBhat, ABhattacharya, KBishai, MBlake, ABolton, TCamilleri, LCaratelli, DTerrazas, ICCarr, RFernandez, RCCavanna, FCerati, GChen, YChurch, ECianci, DCohen, EOCollin, GHConrad, JMConvery, MCooper-Troendle, LCrespo-Anadon, JIDel Tutto, MDevitt, DDiaz, ADuffy, KDytman, SEberly, BEreditato, ASanchez, LEEsquivel, JEvans, JJFadeeva, AAFitzpatrick, RSFleming, BTFranco, DFurmanski, APGarcia-Gamez, DGenty, VGoeldi, DGollapinni, SGoodwin, OGramellini, EGreenlee, HGrosso, RGuenette, RGuzowski, PHackenburg, AHamilton, PHen, OHewes, JHill, CHorton-Smith, GAHourlier, AHuang, E-CJames, CDe Vries, JJJi, XJiang, LJohnson, RAJoshi, JJostlein, HJwa, Y-JKaragiorgi, GKetchum, WKirby, BKirby, MKobilarcik, TKreslo, ILepetic, ILi, YLister, ALittlejohn, BRLockwitz, SLorca, DLouis, WCLuethi, MLundberg, BLuo, XMarchionni, AMarcocci, SMariani, CMarshall, JMartin-Albo, JCaicedo, DAMMastbaum, AMeddage, VMettler, TMistry, KMogan, AMoon, JMooney, MMoore, CDMousseau, JMurphy, MMurrells, RNaples, DNienaber, PNowak, JPalamara, OPandey, VPaolone, VPapadopoulou, APapavassiliou, VPate, SFPavlovic, ZPiasetzky, EPorzio, DPulliam, GQian, XRaaf, JLRafique, ARen, LRochester, LRoss-Lonergan, MVon Rohr, CRRussell, BScanavini, GSchmitz, DWSchukraft, ASeligman, WShaevitz, MHSharankova, RSinclair, JSmith, ASnider, ELSoderberg, MSoldner-Rembold, SSoleti, SRSpentzouris, PSpitz, JSt John, JStrauss, TSutton, KSword-Fehlberg, SSzelc, AMTagg, NTang, WTerao, KThomson, MThornton, RTToups, MTsai, Y-TTufanli, SUsher, TVan De Pontseele, WVan De Water, RGViren, BWeber, MWei, HWickremasinghe, DAWierman, KWilliams, ZWolbers, SWongjirad, TWoodruff, KYang, TYarbrough, GYates, LEZeller, GPZennamo, JZhang, CCollaboration, MWe have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples. |
| spellingShingle | Adams, C Alrashed, M An, R Anthony, J Asaadi, J Ashkenazi, A Auger, M Balasubramanian, S Baller, B Barnes, C Barr, G Bass, M Bay, F Bhat, A Bhattacharya, K Bishai, M Blake, A Bolton, T Camilleri, L Caratelli, D Terrazas, IC Carr, R Fernandez, RC Cavanna, F Cerati, G Chen, Y Church, E Cianci, D Cohen, EO Collin, GH Conrad, JM Convery, M Cooper-Troendle, L Crespo-Anadon, JI Del Tutto, M Devitt, D Diaz, A Duffy, K Dytman, S Eberly, B Ereditato, A Sanchez, LE Esquivel, J Evans, JJ Fadeeva, AA Fitzpatrick, RS Fleming, BT Franco, D Furmanski, AP Garcia-Gamez, D Genty, V Goeldi, D Gollapinni, S Goodwin, O Gramellini, E Greenlee, H Grosso, R Guenette, R Guzowski, P Hackenburg, A Hamilton, P Hen, O Hewes, J Hill, C Horton-Smith, GA Hourlier, A Huang, E-C James, C De Vries, JJ Ji, X Jiang, L Johnson, RA Joshi, J Jostlein, H Jwa, Y-J Karagiorgi, G Ketchum, W Kirby, B Kirby, M Kobilarcik, T Kreslo, I Lepetic, I Li, Y Lister, A Littlejohn, BR Lockwitz, S Lorca, D Louis, WC Luethi, M Lundberg, B Luo, X Marchionni, A Marcocci, S Mariani, C Marshall, J Martin-Albo, J Caicedo, DAM Mastbaum, A Meddage, V Mettler, T Mistry, K Mogan, A Moon, J Mooney, M Moore, CD Mousseau, J Murphy, M Murrells, R Naples, D Nienaber, P Nowak, J Palamara, O Pandey, V Paolone, V Papadopoulou, A Papavassiliou, V Pate, SF Pavlovic, Z Piasetzky, E Porzio, D Pulliam, G Qian, X Raaf, JL Rafique, A Ren, L Rochester, L Ross-Lonergan, M Von Rohr, CR Russell, B Scanavini, G Schmitz, DW Schukraft, A Seligman, W Shaevitz, MH Sharankova, R Sinclair, J Smith, A Snider, EL Soderberg, M Soldner-Rembold, S Soleti, SR Spentzouris, P Spitz, J St John, J Strauss, T Sutton, K Sword-Fehlberg, S Szelc, AM Tagg, N Tang, W Terao, K Thomson, M Thornton, RT Toups, M Tsai, Y-T Tufanli, S Usher, T Van De Pontseele, W Van De Water, RG Viren, B Weber, M Wei, H Wickremasinghe, DA Wierman, K Williams, Z Wolbers, S Wongjirad, T Woodruff, K Yang, T Yarbrough, G Yates, LE Zeller, GP Zennamo, J Zhang, C Collaboration, M Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title | Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title_full | Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title_fullStr | Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title_full_unstemmed | Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title_short | Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber |
| title_sort | deep neural network for pixel level electromagnetic particle identification in the microboone liquid argon time projection chamber |
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deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT piasetzkye deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT porziod deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT pulliamg deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT qianx deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT raafjl deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT rafiquea deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT renl deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT rochesterl deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT rosslonerganm deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT vonrohrcr deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT russellb deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT scanavinig deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT schmitzdw deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT schukrafta deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT seligmanw 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deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT szelcam deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT taggn deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT tangw deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT teraok deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT thomsonm deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT thorntonrt deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT toupsm deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT tsaiyt deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT tufanlis deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT ushert deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT vandepontseelew deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT vandewaterrg deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT virenb deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT weberm deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT weih deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT wickremasingheda deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT wiermank deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT williamsz deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT wolberss deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT wongjiradt deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT woodruffk deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT yangt deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT yarbroughg deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT yatesle deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT zellergp deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT zennamoj deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT zhangc deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber AT collaborationm deepneuralnetworkforpixellevelelectromagneticparticleidentificationinthemicrobooneliquidargontimeprojectionchamber |