Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00639427" target="_blank" >RIV/68378271:_____/25:00639427 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/25:00386651 RIV/00216208:11320/25:10510362
Result on the web
<a href="https://hdl.handle.net/11104/0374707" target="_blank" >https://hdl.handle.net/11104/0374707</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1140/epjc/s10052-025-14313-8" target="_blank" >10.1140/epjc/s10052-025-14313-8</a>
Alternative languages
Result language
angličtina
Original language name
Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning
Original language description
The 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Physical Journal C
ISSN
1434-6044
e-ISSN
1434-6052
Volume of the periodical
85
Issue of the periodical within the volume
6
Country of publishing house
DE - GERMANY
Number of pages
24
Pages from-to
697
UT code for WoS article
001525509600001
EID of the result in the Scopus database
2-s2.0-105016460910