Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

Abratenko, P.; Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadon, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Huang, E. -C.; James, C.; de Vries, J. Jan; Jen, C. -M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Kalousis, L. N.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D. A. Martinez; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; Soldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; John, J. St.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.48550/arxiv.1703.06187

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…[12] and the muon momentum estimator described in Ref. [48], are combined with this generic neutrino selection to perform the analysis.…”

Section: A Reconstruction Workflowmentioning

confidence: 99%

See 1 more Smart Citation

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

Abratenko¹,

An²,

Anthony³

et al. 2022

Phys. Rev. Lett.

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“…[12] and the muon momentum estimator described in Ref. [48], are combined with this generic neutrino selection to perform the analysis.…”

Section: A Reconstruction Workflowmentioning

confidence: 99%

“…quantities (e.g., δp T ) at the cost of a notable drop in efficiency. (VI) Muon momentum estimators based on multipleCoulomb scattering[48] (p MCS µ ) and based on track…”

mentioning

confidence: 99%

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

Abratenko¹,

An²,

Anthony³

et al. 2022

Phys. Rev. Lett.

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“…If the track is contained within the detector, then the muon energy can be determined precisely ( ∼ 0.05) from the length of the track. If the track is only partially contained, however, then the energy can still be estimated, albeit with less precision, from the deflection of the track due to small angle scattering of the muon as it passes through the detector [26,27] ( ∼ 0.23). But these estimates were obtained for charged leptons with E ≤ 3 GeV.…”

Section: Particle Typementioning

confidence: 99%

Neutrino topology reconstruction at DUNE and applications to searches for dark matter annihilation in the Sun

Rott

Jeong

Kumar

et al. 2019

J. Cosmol. Astropart. Phys.

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We consider a new technique for neutrino energy and topology reconstruction at DUNE. In particular, we show that when the direction of the incoming neutrino is known, one can use the measured directions of the outgoing leptonic and hadronic particles to reconstruct poorly-measured quantities, such as the hadronic cascade energy. We show that this alternative technique yields an energy resolution which is comparable to current reconstruction methods which sum measured energies. As a proof of concept we apply this new reconstruction method to a search for dark matter annihilation in the Sun. We show that the use of directional information from both the leptonic and hadronic interaction products allows one to effectively reject backgrounds and isolate the signal, giving competitive sensitivities.

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“…Traditionally, the energies of electrons and hadrons are calculated from calorimetric energies and calibration factors. The kinetic energy of the muons is reconstructed using the length of the track or the multiple Coulomb scattering method [5], depending on whether the track is contained or not inside the detector. The Coulomb scattering method uses the average scattering angle of a muon to predict the energy not contained in the detector.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Kinematic Reconstruction for DUNE

Liu¹,

Ott²,

Collado³

et al. 2020

Preprint

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In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of θ 23 remain unknown. The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment, which aims to address these questions by measuring the oscillation patterns of ν µ /ν e and νµ /ν e over a range of energies spanning the first and second oscillation maxima. DUNE far detector modules are based on liquid argon TPC (LArTPC) technology. A LArTPC offers excellent spatial resolution, high neutrino detection efficiency, and superb background rejection, while reconstruction in LArTPC is challenging. Deep learning methods, in particular, Convolutional Neural Networks (CNNs), have demonstrated success in classification problems such as particle identification in DUNE and other neutrino experiments. However, reconstruction of neutrino energy and final state particle momenta with deep learning methods is yet to be developed for a full AI-based reconstruction chain. To precisely reconstruct these kinematic characteristics of detected interactions at DUNE, we have developed and will present two CNN-based methods, 2-D and 3-D, for the reconstruction of final state particle direction and energy, as well as neutrino energy. Combining particle masses with the kinetic energy and the direction reconstructed by our work, the four-momentum of final state particles can be obtained. Our models show considerable improvements compared to the traditional methods for both scenarios.

show abstract

Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

Cited by 3 publications

References 11 publications

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

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

Neutrino topology reconstruction at DUNE and applications to searches for dark matter annihilation in the Sun

Deep-Learning-Based Kinematic Reconstruction for DUNE

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