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A laserball calibration device for the SNO+ scintillator phase

2024· article· en· W4390973192 on OpenAlexaboutno aff
S. Valder, A. Gibson-Foster, E. Falk, S. J. M. Peeters, C. Mills, M. Nirkko, M. Rigan, J. Sinclair

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersScience and Technology Facilities Council
KeywordsScintillatorCalibrationOpticsDetectorMaterials scienceIsotropyPhysics

Abstract

fetched live from OpenAlex

Abstract Located 2 km underground in SNOLAB, Sudbury, Canada, SNO+ is a large scale liquid scintillator experiment that primarily aims to search for neutrinoless double beta decay. Whilst SNO+ has light and radioactive calibration sources external to the inner volume, an internally deployed optical source is necessary for the full characterization of the detector model. A laser diffuser ball developed for SNO has previously demonstrated to be an effective optical calibration device for both SNO and SNO+ water phase. Since the introduction of liquid scintillator for SNO+, the material compatibility, cleanliness, and radiopurity requirements of any materials in contact with the internal medium have increased. Improving on the original SNO laserball design, a new laserball calibration device has been developed for the SNO+ scintillator phase with the goal of measuring the optical properties of the detector and performing routine PMT gain and timing calibrations. Simulations have been written to model the diffusion properties to optimise optical and temporal performance for calibration. Prototype laserballs have been built and characterised, demonstrating sub-ns timing resolution and a quasi-isotropic light distribution.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.204

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 communication0.0000.001
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 head0.031
GPT teacher head0.384
Teacher spread0.352 · 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 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
Published2024
Admission routes1
Has abstractyes

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