Electromagnetic backgrounds and potassium-42 activity in the DEAP-3600 dark matter detector
Bibliographic record
Abstract
The DEAP-3600 experiment is searching for weakly interacting massive particles dark matter with a $\text{ }3.3\text{ }\ifmmode\times\else\texttimes\fi{}1{0}^{3}\text{kg}$ single phase liquid argon (LAr) target, located 2.1 km underground at SNOLAB. The experimental signature of dark matter interactions is kilo electron volt--scale $^{40}\mathrm{Ar}$ nuclear recoils producing 128 nm LAr scintillation photons observed by photomultiplier tubes. The largest backgrounds in DEAP-3600 are electronic recoils (ERs) induced by $\ensuremath{\beta}$ and $\ensuremath{\gamma}$ rays originating from internal and external radioactivity in the detector material. A background model of the ER interactions in DEAP-3600 was developed and is described in this work. The model is based on several components which are expected from radioisotopes in the LAr, from ex situ material assay measurements, and from dedicated independent in situ analyses. This prior information is used in a Bayesian fit of the ER components to a 247.2 d dataset to model the radioactivity in the surrounding detector materials. Pulse-shape discrimination separates ER and NR events. However, detailed knowledge of the ER background and activity of detector components sets valuable constraints on NR backgrounds including neutrons and alphas. In addition, the activity of $^{42}\mathrm{Ar}$ in LAr in DEAP-3600 is determined by measuring the daughter decay of $^{42}\mathrm{K}$. This cosmogenically activated trace isotope is a relevant background at higher energies for other rare event searches using atmospheric argon, e.g., DarkSide-20k, GERDA, or LEGEND. The specific activity of $^{42}\mathrm{Ar}$ in the atmosphere is found to be $40.4\ifmmode\pm\else\textpm\fi{}5.9\text{ }\text{ }\ensuremath{\mu}\mathrm{Bq}/\mathrm{kg}$ of argon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".