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Record W4376645730 · doi:10.3389/fphy.2023.1181400

SiPM cross-talk in liquid argon detectors

2023· article· en· W4376645730 on OpenAlexaff
M. G. Boulay, V. Camillo, N. Canci, S. Choudhary, L. Consiglio, A. Flammini, C. Galbiati, C. Ghiano, A. Gola, S. Horikawa, P. Kachru, I. Kochanek, K. Kondo, G. Korga, M. Kuźniak, A. Mazzi, A. Moharana, G. Nieradka, G. Paternoster, A. Razeto, D. Sablone, T.N. Thorpe, C. Türkoğlu, H. Wang, M. Rescigno, S. Sanfilippo

Bibliographic record

VenueFrontiers in Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSilicon photomultiplierPhysicsDetectorElectronScintillationOpticsPhotomultiplierFano factorArgonPhotoelectric effectPhotodetectorScintillatorNuclear physicsAtomic physicsShot noise

Abstract

fetched live from OpenAlex

SiPM-based readouts are becoming the standard for light detection in particle detectors given their superior resolution and ease of use with respect to vacuum tube photo-multipliers. However, the contributions of noise detection such as the dark rate, cross-talk, and after-pulsing (AP) may significantly impact their performance. In this work, we present the development of highly reflective single-phase argon chambers capable of displaying light yields up to 32 photo-electrons per keV, with approximately 12 being primary photo-electrons generated by the argon scintillation, while the rest are accounted by optical cross-talk. Furthermore, the presence of compound processes results in a generalized Fano factor larger than 2 already at an over-voltage of 5 V. Finally, we present a parametrization of the optical cross-talk for the FBK NUV-HD-Cryo SiPMs at 87 K that can be extended to future detectors with tailored optical simulations.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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