Reconstruction and measurement of 𝒪(100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

Adams, C.; Nowak, J.; Genty, V.; Zennamo, J.; Guénette, R.; Water, R. G. Van de; Marchionni, A.; Church, E.; Hourlier, A.; Mooney, M.; Esquivel, J.; Sinclair, J.; Barnes, C.; Kreslo, I.; Meddage, V.; Bay, F.; Itay, R.; Cerati, G. B.; Carr, R.; Luo, X.; Baller, B.; Sanchez, L. Escudero; Devitt, D.; Strauss, T.; Johnson, R. A.; Hen, O.; Caicedo, D. A. Martínez; Tsai, Y.-T.; Moore, C. D.; Toups, M.; Furmanski, A. P.; Mariani, C.; Louis, W. C.; Cavanna, F.; Anthony, J.; Mistry, K.; Neely, R. K.; Mason, K.; Palamara, O.; Fitzpatrick, R. S.; Szelc, A. M.; Marcocci, S.; Ketchum, W.; Fernández, R. Castillo; Mohayai, T.; Green, P.; Murphy, M.; Söldner-Rembold, S.; Fleming, B. T.; Yates, L. E.; Hill, Colton; Mogan, A.; Lorca, D.; Thornton, R. T.; Jiang, L.; Shaevitz, M. H.; Dytman, S.; Ji, X.; Evans, Justin J.; Rafique, A.; Cohen, E.; Bass, M.; Sutton, K.; Vries, J. Jan de; Rogers, H. E.; Russell, B.; Gollapinni, S.; Joshi, J.; Dı́az, A.; Stancari, M.; Snider, E. L.; Goeldi, D.; Guzowski, P.; Rohr, C. Rudolf von; Wospakrik, M.; Blake, A.; Tufanli, S.; Duffy, K.; Rochester, Lynn; Woodruff, K.; Grosso, R.; Naples, D.; Qian, X.; Porzio, D.; Horton-Smith, G. A.; Papadopoulou, A.; Viren, B.; Franco, D.; Berkman, S.; Goodwin, O.; McConkey, N.; Li, Y.; Papavassiliou, V.; Kirby, M.; Wickremasinghe, D. A.; Ren, Lu; García-Gámez, D.; Pate, S. F.; Gu, W.; Mastbaum, A.; Eberly, B.; Crespo-Anadón, J. I.; Chen, Y.; Paolone, V.; Williams, Z.; Spitz, J.; Yarbrough, G.; Huang, E.-C.; Hamilton, P.; Piasetzky, E.; Miller, K.; Martín-Albo, J.; Karagiorgi, G.; Murrells, R.; Caratelli, D.; Weber, M.; Bhanderi, A.; Schmitz, D.; Kirby, B.; Seligman, W.; Jo, J. H.; Terrazas, I. Caro; Lockwitz, S.; Balasubramanian, S.; Tagg, N.; Wongjirad, T.; Luethi, M.; Lepetic, I.; Moon, J.; Conrad, J. M.; James, C.; Yang, T.; Bishai, M.; Mills, J.; Marshall, J.; Barr, G.; Wolbers, S.; Asaadi, J.; Paudel, A.; Gramellini, E.; Sword-Fehlberg, S.; Pulliam, G.; Greenlee, H.; Mousseau, J.; Prince, S.; Smith, A.; Mettler, T.; Foppiani, N.; Bhat, A.; Alrashed, M.; Gardiner, S.; Tutto, M. Del; Littlejohn, B. R.; Lundberg, B.; Wu, W.; Zeller, G. P.; Ashkenazi, A.; Lister, A.; Kobilarcik, T.; Terao, K.; Bolton, T.; Cianci, D.; Scanavini, G.; Domine, L.; Nienaber, P.; Soleti, S. R.; John, J. St.; Raaf, J. L.; Schukraft, A.; Zhang, C.; Convery, M. R.; Camilleri, L.; Ereditato, A.; Pandey, V.; Sharankova, R.; Ross-Lonergan, M.; An, R.; Spentzouris, P.; Basque, V.; Gu, L.; Usher, T.; Pontseele, W. Van De; Pavlović, Ž.; Jwa, Y.-J.; Cooper-Troendle, L.; Wei, H.; Soderberg, M.; Tang, W.

doi:10.1088/1748-0221/15/02/p02007

Cited by 35 publications

(26 citation statements)

References 20 publications

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“…The generic neutrino detection procedures described in this article mark the beginning of a high-performance selection of individual neutrino interaction channels, which requires additional particle-level pattern recognition and reconstruction techniques. Several algorithms have been developed in MicroBooNE and applied in previous publications, such as Pandora [56], Deep Learning [57,58], Multiple Coulomb Scattering [46], and electromagnetic shower reconstruction [59]. Additional pattern recognition tools are in development, including those within the Wire-Cell reconstruction.…”

Section: Performance Of the Generic Neutrino Detectionmentioning

confidence: 99%

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

Abratenko¹,

Alrashed²,

An³

et al. 2021

Phys. Rev. Applied

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For a large liquid argon time projection chamber (LArTPC) operating on or near the Earth's surface to detect neutrino interactions, the rejection of cosmogenic background is a critical and challenging task because of the large cosmic ray flux and the long drift time of the TPC. We introduce a superior cosmic background rejection procedure based on the Wire-Cell three-dimensional (3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to cosmic-ray background ratio, we demonstrate these tools on data from the MicroBooNE experiment and create a high performance generic neutrino event selection with a cosmic contamination of 14.9% (9.7%) for a visible energy region greater than O(200) MeV. The neutrino interaction selection efficiency is 80.4% and 87.6% for inclusive νµ charged-current and νe charged-current interactions, respectively. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

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Section: Performance Of the Generic Neutrino Detectionmentioning

confidence: 99%

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

Abratenko¹,

Alrashed²,

An³

et al. 2021

Phys. Rev. Applied

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“…Decays of 0 are a valuable standard candle to constrain the energy scale for electromagnetic showers. Measurement of the reconstructed invariant mass from the two decay photons in LArTPC was demonstrated by MicroBooNE [3].…”

Section: Neutral Pion Decaysmentioning

confidence: 99%

Calibrating the DUNE LArTPC Detectors for Precision Physics

Pec¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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The Deep Underground Neutrino Experiment (DUNE) is an international collaboration focused on studying neutrino oscillation over a long baseline (1300 km). DUNE will make use of a near detector and O(GeV) neutrino beam originating at Fermilab in Batavia, IL, and a far detector operating 1.5 km underground at the Sanford Underground Research Facility in Lead, South Dakota. The near and far detectors will use the LArTPC (Liquid Argon Time Projection Chamber) technology to image neutrino interactions. In order to make precise physics measurements at DUNE, such as the amount of CP violation in the neutrino sector, it is essential to be able to accurately reconstruct particle energies and other kinematic quantities; this in turn necessitates an extensive calibration program for DUNE's LArTPC detectors. In this presentation, we describe the requirements for calibrating the DUNE detectors, emphasizing the challenges of massive multikiloton LArTPC detectors which are to operate for multiple years deep underground. A preliminary DUNE detector calibration program, including use of both dedicated calibration hardware and cosmogenic/beam-induced calibration sources, is presented. First results on detector calibration at the ProtoDUNE-SP prototype detector located at CERN, and associated impact on calibrations at the DUNE far detector, are also emphasized.

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“…The detector [6] is a 10.4 m long, * microboone info@fnal.gov the shower exceeds the detector boundaries. This cascade generates a pattern referred to as a shower [8]. The lower the electromagnetic shower energy is, the less branches are created and its topology becomes less distinct from a track.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

Abratenko¹,

Alrashed²,

An³

et al. 2021

Phys. Rev. D

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We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps.This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine. 42 eSSNet plays an important role in many tasks such as 43 vertex finding, shower reconstruction and energy recon-44 struction, and neutrino selection in the DL-LEE search in 45 MicroBooNE, and is the first usage of SSCN in LArTPC 46 data (beam-off) and realistic MC (neutrino interactions).

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Reconstruction and measurement of 𝒪(100) MeV energy electromagnetic activity from π⁰ arrow γγ decays in the MicroBooNE LArTPC

Cited by 35 publications

References 20 publications

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

Calibrating the DUNE LArTPC Detectors for Precision Physics

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

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