An investigation of the effects of wavelength shifter thickness on backgrounds for the DEAP-3600 dark matter direct detection experiment
Bibliographic record
Abstract
Several lines of astronomical evidence indicate that most of the matter in our universe is made of non-luminous material known as dark matter, the nature of which remains a mystery. DEAP-3600 is a liquid-argon-based scintillation detector located 2 km underground outside Sudbury, Ontario, Canada, purposed towards directly detecting dark matter particles as they pass through the target material. Since the acrylic vessel containing the 3600-kg liquid argon target is not transparent to the 128-nm scintillation light produced during these interactions, a thin layer of 1,1,4,4-tetraphenyl-1,3-butadiene (TPB) was deposited onto the inner surface of the acrylic vessel to shift the wavelength of the scintillation light to the visible range, where is can pass through the transparent acrylic vessel and is more easily detected by the surrounding array of photomultiplier tubes. The dark matter signal in DEAP-3600 is accompanied by a host of other background signals, which must either be suppressed, or identified as background signals and subsequently cut from the analysis. A major anticipated source of background events is alpha decays of radioactive isotopes on the inner surface of the acrylic vessel, near the TPB layer. The potential for these events to generate a background signal depends sensitively on the choice of thickness of the TPB layer. A study of the effects of TPB layer thickness on the background rate of these surface alpha events is presented, and a model for the dominant contributor to these background events is developed to generate estimates of the probability that an event of this type would mimic a dark matter signal. Other effects considered in the choice of TPB layer thickness are presented, and the final decision of a 2.99 +- 0.02 micron TPB layer thickness, along with the TPB deposition campaign, is briefly described.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".