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Photomultiplier requirements and pre-calibration for the SABRE South Liquid Scintillator Veto

2025· article· en· W4412751578 on OpenAlexaff
L. J. Milligan, P. Urquijo, E. L. Barberio, V. U. Bashu, L.J. Bignell, I. Bolognino, S.S. Chhun, F. Dastgiri, T. Fruth, Guang-Yong Fu, G. C. Hill, R. S. James, K. Janssens, Sanjana Kapoor, G. J. Lane, K. T. Leaver, P. McGee, L.J. McKie, John A. McKenzie, P. C. McNamara, W. J. D. Melbourne, M. Mews, Wing H. Ng, K.J. Rule, Z. Slavkovská, Matthew Stanley, A. E. Stuchbery, B. Suerfu, G. N. Taylor, D. Tempra, Thomas Tunningley, Anthony G. Williams, Yang Zhong, M. J. Zurowski

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of Toronto
FundersUniversity of MelbourneAustralian Government
KeywordsScintillatorPhotomultiplierCalibrationVetoNuclear engineeringNuclear physicsPhysicsOpticsMaterials scienceDetectorEngineering

Abstract

fetched live from OpenAlex

Abstract We present a study of the oil-proof base Hamamatsu R5912 photomultiplier tubes that will be used in the SABRE South linear-alkylbenzene liquid scintillator veto. SABRE South is a dark matter direct detection experiment at the Stawell Underground Physics Laboratory, aiming to test the DAMA/LIBRA dark matter annual modulation signal. We discuss the requirements of the liquid scintillator system and its photomultipliers, outline the methods and analysis used for the characterisation measurements, and results from initial tests. We discuss the impact of these measurements on the performance of the active veto system and explore analysis methods to allow for low threshold operation. Finally, we include results from a small scale liquid scintillator detector prototype used to assess the future performance of pulse shape discrimination in the liquid scintillator veto, and how well accommodated it is by the R5912 PMTs.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.186

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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 designOther design
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

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