Novel Approach for Evaluating Detector-Related Uncertainties in a LArTPC Using MicroBooNE Data
MicroBooNE collaboration, P. Abratenko, R. An, J. Anthony, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L… 展开作者
MicroBooNE collaboration, P. Abratenko, R. An, J. Anthony, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, J. Y. Book, L. Camilleri, D. Caratelli, I. Caro Terrazas, F. Cavanna, G. Cerati, Y. Chen, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, S. R. Dennis, P. Detje, A. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, J. J. Evans, R. Fine, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. Garcia-Gamez, S. Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, O. Hen, C. Hilgenberg, G. A. Horton-Smith, A. Hourlier, R. Itay, C. James, X. Ji, L. Jiang, J. H. Jo, R. A. Johnson, Y. -J. Jwa, D. Kalra, N. Kamp, N. Kaneshige, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, I. Lepetic, K. Li, Y. Li, K. Lin, B. R. Littlejohn, W. C. Louis, X. Luo, K. Manivannan, C. Mariani, D. Marsden, J. Marshall, D. A. Martinez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage, T. Mettler, K. Miller, J. Mills, K. Mistry, A. Mogan, T. Mohayai, J. Moon, M. Mooney, A. F. Moor, C. D. Moore, L. Mora Lepin, J. Mousseau, M. Murphy, D. Naples, A. Navrer-Agasson, M. Nebot-Guinot, R. K. Neely, D. A. Newmark, J. Nowak, M. Nunes, O. Palamara, V. Paolone, A. Papadopoulou, V. Papavassiliou, S. F. Pate, N. Patel, A. Paudel, Z. Pavlovic, E. Piasetzky, I. D. Ponce-Pinto, S. Prince, X. Qian, J. L. Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. C. J. Rice, L. Rochester, J. Rodriguez Rondon, M. Rosenberg, M. Ross-Lonergan, G. Scanavini, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Shi, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Söldner-Rembold, P. Spentzouris, J. Spitz, M. Stancari, J. St. John, T. Strauss, K. Sutton, S. Sword-Fehlberg, A. M. Szelc, W. Tang, K. Terao, C. Thorpe, D. Totani, M. Toups, Y. -T. Tsai, M. A. Uchida, T. Usher, W. Van De Pontseele, B. Viren, M. Weber, H. Wei, Z. Williams, S. Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, N. Wright, W. Wu, E. Yandel, T. Yang, G. Yarbrough, L. E. Yates, H. W. Yu, G. P. Zeller, J. Zennamo, C. Zhang
Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.
Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation
Measurement of the Flux-Averaged Inclusive Charged-Current Electron Neutrino and Antineutrino Cross Section on Argon using the NuMI Beam and the MicroBooNE Detector
A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
Yale University（耶鲁大学）；Kansas State University（堪萨斯州立大学）；Illinois Institute of Technology（伊利诺伊理工学院）；University of Cambridge（剑桥大学）；University of Texas, Arlington（德克萨斯大学阿灵顿分校）；Massachusetts Institute of Technology（麻省理工学院）；Universität Bern（伯尔尼大学）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；University of Michigan（密歇根大学）；University of Oxford（牛津大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；TUBITAK Space Technologies Research Institute（土耳其科学技术研究理事会空间技术研究所）；Syracuse University（雪城大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Lancaster University（兰卡斯特大学）
First application of a liquid argon time projection chamber for the search for intranuclear neutron-antineutron transitions and annihilation in $^{40}$Ar using the MicroBooNE detector
First Measurement of Charged Current Muon Neutrino-Induced $K^+$ Production on Argon using the MicroBooNE Detector
Tufts University（塔夫茨大学）；Illinois Institute of Technology (IIT)（伊利诺伊理工学院）；The University of Manchester（曼彻斯特大学）；University of Texas, Arlington（德克萨斯大学阿灵顿分校）；Tel Aviv University（特拉维夫大学）；Fermi National Accelerator Laboratory (FNAL)（费米国家加速器实验室）；University of Oxford（牛津大学）；University of Minnesota（明尼苏达大学）；Imperial College London（伦敦帝国学院）；Michigan State University（密歇根州立大学）；University of Chicago（芝加哥大学）；Brookhaven National Laboratory (BNL)（布鲁克海文国家实验室）
Measurement of double-differential cross sections for mesonless charged-current muon neutrino interactions on argon with final-state protons using the MicroBooNE detector
First simultaneous measurement of differential muon-neutrino charged-current cross sections on argon for final states with and without protons using MicroBooNE data
