First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

Abratenko, P.; An, R.; Anthony, J.; Arellano, Luis Ervin Prado; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Rodrigues, O. Benevides; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Book, Julia; Camilleri, L.; Caratelli, D.; Terrazas, I. Caro; Fernández, R. Castillo; Cavanna, F.; Cerati, G. B.; Chen, Y.; Cianci, D.; Conrad, J. M.; Convery, M. R.; Cooper-Troendle, L.; Crespo-Anadón, J. I.; Tutto, M. Del; Dennis, S. R.; Detje, P.; Devitt, Ann; Diurba, R.; Dorrill, R.; Duffy, K.; Dytman, S. A.; Eberly, B.; Ereditato, A.; Evans, Justin J.; Fine, R.; Aguirre, G. A. Fiorentini; Fitzpatrick, R. S.; Fleming, B. T.; Foppiani, N.; Franco, D.; Furmanski, A. P.; García-Gámez, D.; Gardiner, S.; Ge, G.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Green, P.; Greenlee, H.; Gu, W.; Guénette, 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.; Kalra, D.; Kamp, N.; Kaneshige, N.; Karagiorgi, G.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; LaZur, R.; 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. Martínez; Mason, K.; Mastbaum, A.; McConkey, N.; Meddage, V.; Mettler, T.; Miller, K.; Mills, J.; Mistry, K.; Mohayai, T.; Mogan, A.; Moon, J.; Mooney, M.; Moor, A. F.; Moore, C. D.; Lepin, L. Mora; Mousseau, J.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Nebot-Guinot, M.; Neely, R. K.; Newmark, D. A.; Nowak, J.; Nunes, M. Soares; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Patel, N. D.; Paudel, A.; Pavlović, Ž.; Piasetzky, E.; Ponce-Pinto, I. D.; Prince, S.; Qian, X.; Raaf, J. L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, Lu; Rice, L. C. J.; Rochester, Lynn; Rondon, J. Rodriguez; Rosenberg, M.; Ross-Lonergan, M.; Scanavini, G.; Schmitz, D.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Shi, J. Y.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Spentzouris, P.; Spitz, J.; Stancari, M.; John, J. St.; 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.; Pontseele, W. Van De; 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, Hai-Bo; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.1103/physrevlett.128.151801

Cited by 22 publications

(12 citation statements)

References 219 publications

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“…To summarize, we describe a set of pattern recognition algorithms to reconstruct neutrino events based on the Wire-Cell reconstruction paradigm. These algorithms perform well on the tasks of neutrino vertex identification, single particle reconstruction, and neutrino energy reconstruction, which are essential to achieve high-performance inclusive CC and CC event selections [23,24]. Evaluated using the current state-of-the-art MicroBooNE MC simulation, we achieve 65.8% and 72.9% neutrino vertex identification efficiencies for and charged-current interactions, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, 80-90% single particle reconstruction efficiencies for primary leptons and 15-20% reconstructed neutrino energy resolutions are achieved for these charged-current neutrino interactions. The achievements reported in this paper build a solid foundation for downstream physics analyses, such as the search for new physics with electron neutrinos and measurement [23] and measurement of charged-current muon neutrino interaction cross section on Argon [24]. Regarding the performance comparison between the Wire-Cell reconstruction and other reconstruction paradigms in MicroBooNE, more information can be found in Ref.…”

Section: Discussionmentioning

confidence: 99%

“…In real data analysis [23,24], the various methods used to reconstruct neutrino energy are validated. The summation of / method is validated by comparing the / vs. residual range for the stopped muons and protons between data and MC.…”

Section: Neutrino Energy Reconstructionmentioning

confidence: 99%

“…MicroBooNE data (run 5384, event 2561) This paper summarizes the pattern recognition techniques developed and applied in Wire-Cell for the high-performance inclusive CC and CC event selections [23,24]. Both traditional algorithms (non-machine-learning algorithms) and machine-learning algorithms are used in this pattern recognition procedure.…”

Section: Introductionmentioning

confidence: 99%

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Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Abratenko¹,

An²,

Anthony³

et al. 2022

J. Inst.

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Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Neutrino Energy Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Abratenko¹,

An²,

Anthony³

et al. 2022

J. Inst.

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“…In the cross-section extraction, we use a number of nucleons equal to 4.3912 × 10 31 , and a POT-integrated BNB ν e flux of 2.73 × 10 9 cm −2 , which is taken to be the reference flux [42] of the measurement and used as a constant value. As described in a previous MicroBooNE publication [43], this method allows for a consistent treatment FIG. 1.…”

mentioning

confidence: 99%

Differential cross section measurement of charged current $ν_{e}$ interactions without final-state pions in MicroBooNE

collaboration¹,

Abratenko²,

Anthony³

et al. 2022

Preprint

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Inclusive quasielastic neutrino-nucleus scattering with energy density functional nuclear models*

Kim,

Choi,

Gil

et al. 2024

Chinese Phys. C

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In the quasielastic region, we calculate the inclusive charged-current neutrino-nucleus scattering from $^{40}$Ar.To explore the effect of uncertainties stemming from the nuclear structure, we use the KIDS (Korea-IBS-Daegu-SKKU) nuclear energy density functional and Skyrme force models, SLy4, SkI3, and MSk7.The models are selected to have distinct behavior of the density dependence of the symmetry energy and the effective mass of the nucleon.In the charged-current neutrino scattering, the single- and double-differential cross sections are calculated in various kinematics and total cross sections are shown in terms of the incident neutrino energy.The theoretical cross sections are compared with experimental data, andthe roles of the effective mass and the symmetry energy are investigated in the charged-current neutrino-nucleus scattering.

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First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

Cited by 22 publications

References 219 publications

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Differential cross section measurement of charged current $ν_{e}$ interactions without final-state pions in MicroBooNE

Inclusive quasielastic neutrino-nucleus scattering with energy density functional nuclear models*

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