2022 Articles

Novel approach for evaluating detector-related uncertainties in a LArTPC using MicroBooNE data

Abratenko, P.; An, R.; Anthony, J.; Arellano, L.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Book, J. Y.; Camilleri, L.; Caratelli, D.; Caro Terrazas, I.; Cavanna, F.; Cerati, G.; Chen, Y.; Cianci, D.; Conrad, J. M.; Convery, M.; Cooper-Troendle, L.; Crespo-Anadón, J. I.; Del Tutto, M.; Dennis, S. R.; Detje, P.; Devitt, A.; Diurba, R.; Dorrill, R.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Evans, J. J.; Fine, R.; Fiorentini Aguirre, G. A.; Fitzpatrick, R. S.; Fleming, B. T.; Foppiani, N.; Franco, D.; Furmanski, A. P.; Garcia-Gamez, D.; Gardiner, S.; Ge, G.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Green, P.; Greenlee, H.; Gu, W.; Guenette, R.; Guzowski, P.; Hagaman, L.; Hen, O.; Hilgenberg, C.; Horton-Smith, G. A.; Hourlier, A.; Itay, R.; James, C.; Ji, X.; Jiang, L.; Jo, J. H.; Johnson, R. A.; Jwa, Y.-J.; Kalra, D.; Kamp, N.; Kaneshige, N.; Karagiorgi, G.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Lepetic, I.; Li, K.; Li, Y.; Lin, K.; Littlejohn, B. R.; Louis, W. C.; Luo, X.; Manivannan, K.; Mariani, C.; Marsden, D.; Marshall, J.; Caicedo, D. A. M.; Mason, K.; Mastbaum, A.; McConkey, N.; Meddage, V.; Mettler, T.; Miller, K.; Mills, J.; Mistry, K.; Mogan, A.; Mohayai, T.; Moon, J.; Mooney, M.; Moor, A. F.; Moore, C. D.; Mora Lepin, L.; Mousseau, J.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Nebot-Guinot, M.; Neely, R. K.; Newmark, D. A.; Nowak, J.; Nunes, M.; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Patel, N.; Paudel, A.; Pavlovic, Z.; Piasetzky, E.; Ponce-Pinto, I. D.; Prince, S.; Qian, X.; Raaf, J. L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, L.; Rice, L. C. J.; Rochester, L.; Rodriguez Rondon, J.; Rosenberg, M.; Ross-Lonergan, M.; Scanavini, G.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Shi, J.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Spentzouris, P.; Spitz, J.; Stancari, M.; St. John, J.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tang, W.; Terao, K.; Thorpe, C.; Totani, D.; Toups, M.; Tsai, Y.-T.; Uchida, M. A.; Usher, T.; Van De Pontseele, W.; Viren, B.; Weber, M.; Wei, H.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Wospakrik, M.; Wresilo, K.; Wright, N.; Wu, W.; Yandel, E.; Yang, T.; Yarbrough, G.; Yates, L. E.; Yu, H. W.; Zeller, G. P.; Zennamo, J.; Zhang, C.

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.