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Record W4411593148 · doi:10.1016/j.nima.2025.170755

Development and characterization of the JNE concentrator

2025· article· en· W4411593148 on OpenAlexaff
Shu Xin Ouyang, Yuzi Yang, Aiqiang Zhang, Haoyan Yang, Yuhao Liu, Zhe Wang, Jianmin Li, Zongyi Wang, Shaomin Chen

Bibliographic record

VenueNuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsInstitute of Particle Physics
FundersCAS Center for Excellence in Particle PhysicsState Key Laboratory of New Ceramics and Fine ProcessingChina Postdoctoral Science FoundationShandong UniversityMinistry of Science and Technology of the People's Republic of ChinaTsinghua UniversityNational Natural Science Foundation of China
KeywordsCharacterization (materials science)ConcentratorComputer scienceMaterials scienceNanotechnologyTelecommunications

Abstract

fetched live from OpenAlex

The Jinping Neutrino Experiment (JNE) plans to deploy approximately 3000 8-inch MCP-PMTs for neutrino detection. To enhance photon collection efficiency while ensuring cost-effectiveness, we have developed custom-designed light concentrators with a selected cutoff angle of $70^\circ$, mounted on each PMT. We conducted angular response measurements of the concentration factor at four wavelengths in air. The results are in good agreement with Monte Carlo simulations, thereby validating the optical performance of the design. Under parallel light source, the concentrators enhance light collection efficiency by approximately $40\%$, accompanied by a marginal increase in transit-time spread (with FWHM increase < $0.3$ ns). These findings demonstrate that the proposed concentrator design is both effective and practical for use in JNE, providing significant enhancement in photon collection with minimal timing degradation.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.039
GPT teacher head0.373
Teacher spread0.334 · 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

Citations0
Published2025
Admission routes1
Has abstractno

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Same venueNuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated EquipmentSame topicNeutrino Physics ResearchFrench-language works237,207