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Scintillation light detection performance for the DUNE ND-LAr 2 × 2 modules

2023· article· en· W4360797089 on OpenAlexfundno aff
A. Gauch

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersFermilabInstitut National de Physique Nucléaire et de Physique des ParticulesScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeOffice of ScienceMinisterio de Ciencia e InnovaciónFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean Regional Development FundU.S. Department of EnergyEuropean CommissionCentre National de la Recherche ScientifiqueJunta de AndalucíaFundação de Amparo à Pesquisa do Estado de GoiásNational Science FoundationRoyal SocietyXunta de GaliciaCERNFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado de São PauloSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUK Research and Innovation
KeywordsScintillationPhysicsTime projection chamberDetectorOpticsScintillation counterCosmic rayPixelNeutrinoNeutrino detectorNuclear physicsNeutrino oscillation

Abstract

fetched live from OpenAlex

Abstract The Deep Underground Neutrino Experiment (DUNE) will be using a liquid argon time projection chamber (LAr TPC) with optically separated modules in the Near Detector (ND) complex. A prototype experiment, DUNE ND-LAr 2 × 2, is composed of four test modules. They detect ionization charge through a pixel-based readout and scintillation light through fibers in light collection modules and light traps called ArCLights. The light detection performance for two modules of DUNE ND-LAr 2 × 2 that took cosmic ray data at the University of Bern is shown. We present further the role of the 2 × 2 prototype in DUNE and how it is used to demonstrate the reconstruction capabilities of its light detectors in terms of energy thresholds and timing resolution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.301
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2023
Admission routes1
Has abstractyes

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