MétaCan
Menu
Back to cohort

Trigger performance of plastic scintillator detectors with silicon photomultiplier readout for cosmic ray muons

2025· article· en· W4412149428 on OpenAlexaff
Yiyue Li, Huiling Li, Cong Liu, Songqing Liu, Chaolin Lv, Weiwei Xu, Minghui Zhang

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsScintillatorSilicon photomultiplierPhotomultiplierCosmic rayDetectorMuonPhysicsNuclear physicsScintillation counterOpticsOptoelectronics

Abstract

fetched live from OpenAlex

Abstract A plastic scintillating fiber (SciFi) detector with multi-channel readout electronics based on the Citiroc1A chip is being developed for muon tomography application. In this study, to provide external triggers of cosmic ray muons to the SciFi detector, we fabricated two 10 cm × 10 cm × 2 cm plastic scintillators with wavelength shifting fibers (WLS) and evaluated their trigger performance with silicon photomultiplier readout. The results showed that the plastic scintillators embedded with WLS fibers obtained a light collection increase of up to three times in comparison to those without WLS fibers, and exhibited good discrimination of cosmic ray muons from ambient gamma rays. The trigger system consisted of the two scintillator detectors could achieve a high trigger efficiency up to 99% within a coincidence time window of 10 ns. Its inherent latency of both trigger strategy and readout electronics was well below 30 ns, making it feasible to offer external triggers to Citiroc1A with a peaking time tunable from 12.5 ns to 87.5 ns.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.309

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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 designObservational
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 abstractyes

Explore more

Same venueJournal of InstrumentationSame topicParticle Detector Development and PerformanceFrench-language works237,207