Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

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dc.authorid0000-0003-3716-5100
dc.authorid0000-0003-4322-9246
dc.authorid0000-0002-2742-9718
dc.authorid0000-0003-2020-8215
dc.authorid0000-0001-8192-0826
dc.authorid0000-0003-0057-8796
dc.authorid0000-0002-4784-9867
dc.contributor.authorAbud, A. Abed
dc.contributor.authorAcciarri, R.
dc.contributor.authorAcero, M. A.
dc.contributor.authorAdames, M. R.
dc.contributor.authorAdamov, G.
dc.contributor.authorAdamowski, M.
dc.contributor.authorAdams, D.
dc.date.accessioned2026-01-31T15:08:42Z
dc.date.available2026-01-31T15:08:42Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThe Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.
dc.description.sponsorshipFermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility; LLC (FRA) [DE-AC02-07CH11359]; CNPq; FAPERJ; FAPEG; FAPESP, Brazil; CFI; NSERC, Canada; MSMT, Czech Republic; ERDF; Horizon Europe, MSCA; European Union; CEA, France; INFN, Italy; NRF, South Korea; Generalitat Valenciana, Junta de Andalucia-FEDER; MICINN; Xunta de Galicia, Spain; SNSF, Switzerland; TUBITAK, Turkey; Royal Society; DOE; NSF, United States of America
dc.description.sponsorshipThis document was prepared by the DUNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, IPP and NSERC, Canada; CERN; MSMT, Czech Republic; ERDF, Horizon Europe, MSCA and NextGenerationEU, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; Generalitat Valenciana, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain; SERI and SNSF, Switzerland; TUBITAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America.
dc.identifier.doi10.1140/epjc/s10052-025-14313-8
dc.identifier.issn1434-6044
dc.identifier.issn1434-6052
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105016460910
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.1140/epjc/s10052-025-14313-8
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10735
dc.identifier.volume85
dc.identifier.wosWOS:001525509600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEuropean Physical Journal C
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.titleNeutrino interaction vertex reconstruction in DUNE with Pandora deep learning
dc.typeArticle

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