SPE analysis of high efficiency PMTs for the DEAP-3600 dark matter detector
Bibliographic record
Abstract
The Dark matter Experiment using Argon Pulse-shape discrimination is a collaborative effort to develop a next-generation, tonne-scale dark matter detector at SNOLAB. The detector will feature a single-phase liquid argon (LAr) target surrounded by an array of 266 photomultiplier tubes (PMTs). A new high-efficiency Hamamatsu R877-100 PMT has been delivered to the University of Alberta for evaluation by the DEAP collaboration. The increase in efficiency could lead to a much greater light yield, but other experiments have reported a slower rise time [1],[2]. We have placed the PMT in a small dark box and had a base and preamplifier designed to be used with either an oscilloscope or a multi-channel analyzer. With this setup we have demonstrated the PMT's ability to distinguish single photo-electrons (SPE) and characterized the PMT by measuring the SPE pulse height spectrum, the peak-to-valley ratio, the dark pulse rate, the baseline, time resolution and SPE efficiency for varying the high voltage supplied to the PMT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".