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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. L. Adams, M. Adinolfi, A. Aduszkiewicz, J. Aguilar, Zahid Ahmad, Jamal Ahmed, B. Aimard, B. Ali-Mohammadzadeh, T. Alion, K. K. Allison, S. Alonso Monsalve, M. AlRashed, C. Alt, R. Alvarez, P. Amedo, J. Anderson, C Andreopoulos, M. Andreotti, M. P. Andrews, F. Andrianala, S. Andringa, Н. Анфимов, Artur M. Ankowski, M. Antoniassi, M. Antonova, A. Antoshkin, Stefan Antusch, Alfredo Aranda, L. Arellano, L. O. Arnold, M. A. Arroyave, J. Asaadi, L. Asquith, A. Aurisano, V. Aushev, D. Autiero, V. Lara, M. Ayala-Torres, F. Azfar, M. Babicz, H.O. Back, H. Back, J. J. Back, C. Backhouse, I. Bagaturia, L. Bagby, N. Balashov, S. Balasubramanian, Pierre Baldi, B. Baller, Bindu A. Bambah, F. Barão, Gabriela Barenboim, Gareth J. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, A. L. F. de Barros, N. Barros, J. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, C. Batchelor, E. Batista das Chagas, James Battat, F. Battisti, F. Bay, M. C. Q. Bazetto, J. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, C. Beigbeder, L. Bellantoni, G. Bellettini, Vincenzo Bellini, O. Beltramello, N. Benekos, C. Benitez Montiel, F. Neves, J. Berger, S. Berkman, P. Bernardini, R. M. Berner, A. Bersani, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, A. J. Bevan, Y. Bezawada, T. S. Bezerra, A. Bhardwaj, V. Bhatnagar, M. Bhattacharjee, D. Bhattarai, S. Bhuller, B. Bhuyan, S. Biagi, J. M. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. d. M. Blaszczyk, G. Blazey, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, M. Bongrand, Cesar Bonilla, F. Bonini, Andrew Booth, Fatma Boran, S. Bordoni, A. Borkum, N. Bostan, P. Bour, C. Bourgeois, D. Boyden, J. Braciník, D. Braga, D. Brailsford, A. Branca, A. Brandt, J. Bremer, D. Breton, C. Brew, S. J. Brice, C. Brizzolari, C. Bromberg, J. J. Brooke, A. Bross, G. 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Bibliographic record
VenueThe European Physical Journal C · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsYork UniversityUniversity of Toronto
FundersHigh Energy PhysicsInstituto Superior TécnicoRussian Academy of SciencesNatural Sciences and Engineering Research Council of CanadaInstitut National de Physique Nucléaire et de Physique des ParticulesScience and Technology Facilities CouncilHorizon 2020 Framework ProgrammeInstitut de Física d'Altes EnergiesIstituto Nazionale di Fisica NucleareIndian Institute of Technology GuwahatiJunta de AndalucíaXunta de GaliciaUniversidade de LisboaFermilabUK Research and InnovationNational Science FoundationUniversità degli Studi dell'InsubriaInstituto Nazionale di Fisica NucleareEuropean CommissionMinisterio de Ciencia e InnovaciónFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroImperial College LondonConselho Nacional de Desenvolvimento Científico e TecnológicoNederlandse Organisatie voor Wetenschappelijk OnderzoekCentre National de la Recherche ScientifiqueEuropean Regional Development FundIowa State UniversityMinisterstvo Školství, Mládeže a TělovýchovySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversitat de ValènciaIstituto Nazionale di Fisica Nucleare Sezione di PadovaFundação de Amparo à Pesquisa do Estado de São PauloInstitute for Research in Fundamental SciencesU.S. Department of EnergyFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado de GoiásRoyal SocietyNational Energy Research Scientific Computing CenterCERNOffice of ScienceUniversity of HyderabadIdaho State University
KeywordsDetectorNeutrinoConvolutional neural networkTime projection chamberTrack (disk drive)TraverseEnergy (signal processing)Neutrino detectorPhysicsArtificial neural networkComputer scienceElectronPixelProjection (relational algebra)Nuclear physicsAlgorithmArtificial intelligenceOpticsGeologyNeutrino oscillationGeodesy
Abstract
Abstract Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.361
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.015
GPT teacher head0.240
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations11
Published2022
Admission routes2
Has abstractyes