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Production and testing of the large-area photon detector ArCLight

2023· article· en· W4379793908 on OpenAlexfundno aff
J. Kunzmann

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersHigh Energy PhysicsHorizon 2020 Framework ProgrammeInstitut National de Physique Nucléaire et de Physique des ParticulesScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaOffice of ScienceEuropean CommissionMinisterio 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 EnergyCentre National de la Recherche ScientifiqueJunta de AndalucíaFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado de GoiásFermilabUK Research and InnovationNational Science FoundationRoyal SocietyXunta de GaliciaCERNFundação de Amparo à Pesquisa do Estado de São PauloSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDetectorScintillationTime projection chamberOpticsVolume (thermodynamics)PhotonTrap (plumbing)PhotomultiplierNeutrinoPhysicsModular designOptoelectronicsComputer scienceNuclear physics

Abstract

fetched live from OpenAlex

Abstract Compact large-area dielectric photon detectors that ensure modular noble liquid detectors can sense scintillation light signals for triggering and reconstruction purposes with minimal impact to the overall target volume. ArCLight is a small-volume, sizeable sensitive-area detector that consists of a light trap covered in a thin layer of TPB that can be placed inside a time projection chamber's electric field. These sensors are used in the Deep Underground Neutrino Experiment Near Detector liquid argon (DUNE ND-LAr). They are constructed and tested at the University of Bern and will be deployed in the prototype experiment ProtoDUNE-ND. The talk discusses the details of assembling ArCLights, the quality control methods, and the test studies done with LEDs to assess their performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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