{"paper":{"title":"Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ex","authors_text":"A. Ashkenazi, A. Bhanderi, A. Bhat, A. Blake, A. Devitt, A. Ereditato, A.F. Moor, A. Hourlier, A. Mastbaum, A. Mogan, A.M. Szelc, A. Navrer-Agasson, A. Papadopoulou, A. Paudel, A.P. Furmanski, A. Rafique, A. Schukraft, A. Smith, B. Baller, B. Eberly, B.R. Littlejohn, B.T. Fleming, B. Viren, C. Barnes, C.D. Moore, C. Hilgenberg, C. James, C. Mariani, C.Thorpe, C. Zhang, D.A. Martinez Caicedo, D.A. Newmark, D. Caratelli, D. Cianci, D. Franco, D. Garcia-Gamez, D. Kalra, D. Marsden, D. Naples, D. Totani, D.W. Schmitz, E. Church, E. Gramellini, E.L. Snider, E. Piasetzky, E. Yandel, F. Cavanna, F.J. Yu, G.A. Fiorentini Aguirre, G.A. Horton-Smith, G. Barr, G. Cerati, G. Ge, G. Karagiorgi, G.P. Zeller, G. Scanavini, G. Yarbrough, H. Greenlee, H. Wei, H.W. Yu, I. Caro Terrazas, I. Kreslo, I. Lepetic, I. Ponce-Pinto, J. Anthony, J. Asaadi, J. Barrow, J.H. Jo, J.I. Crespo-Anadon, J.J. Evans, J.L. Raaf, J. Marshall, J.M. Conrad, J. Mills, J. Moon, J. Mousseau, J. Nowak, J. Rodriguez Rondon, J. Shi, J. Sinclair, J. Spitz, J. St. John, J.Y. Book, J.-Y. Li, J. Zennamo, K. Duffy, K. Li, K. Lin, K. Manivannan, K. Mason, K. Miller, K. Mistry, K. Sutton, K. Terao, K. Wresilo, L. Arellano, L. Bathe-Peters, L. Camilleri, L.C.J. Rice, L. Cooper-Troendle, L.E. Yates, L. Hagaman, L. Jiang, L. Mora Lepin, L. Ren, L. Rochester, M.A. Uchida, M. Bishai, M. Convery, M. Del Tutto, M.H. Shaevitz, MicroBooNE collaboration: P. Abratenko, M. Kirby, M. Mooney, M. Murphy, M. Nebot-Guinot, M. Nunes, M. Reggiani-Guzzo, M. Rosenberg, M. Ross-Lonergan, M. Soderberg, M. Stancari, M. Toups, M. Weber, M. Wospakrik, N. Foppiani, N. Kamp, N. Kaneshige, N. McConkey, N. Patel, N. Wright, O. Benevides Rodrigues, O. Goodwin, O. Hen, O. Palamara, P. Detje, P. Green, P. Guzowski, P. Spentzouris, R.A. Johnson, R. An, R. Diurba, R. Dorrill, R. Fine, R. Guenette, R. Itay, R.K. Neely, R.S. Fitzpatrick, R. Sharankova, S. Balasubramanian, S. Berkman, S. Dytman, S.F. Pate, S. Gardiner, S. Gollapinni, S. Mulleria Babu, S. Prince, S.R. Dennis, S. Soldner-Rembold, S. Sword-Fehlberg, S. Wolbers, T. Bolton, T. Kobilarcik, T. Mettler, T. Mohayai, T. Strauss, T. Usher, T. Wongjirad, T. Yang, V. Basque, V. Meddage, V. Paolone, V. Papavassiliou, V. Radeka, W.C. Louis, W. Gu, W. Ketchum, W. Seligman, W. Tang, W. Van De Pontseele, W. Wu, X. Ji, X. Luo, X. Qian, Y. Chen, Y.J. Jwa, Y. Li, Y.-T. Tsai, Z. Pavlovic, Z. Williams","submitted_at":"2022-01-14T23:08:00Z","abstract_excerpt":"In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05705","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.05705/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}