Bibliographic record
Abstract
The Dark matter Experiment using Argon Pulseshape discrimination (DEAP) utilizes a single-phase liquid argon (LAr) detector aiming to acquire the scintillation light produced by nuclear recoils expected to be induced by Weakly Interacting Massive Particle (WIMP) candidate of dark matter. The DEAP-3600 detector consists of about 3.3 tonnes of LAr in a spherical acrylic vessel viewed by 255 photomultiplier tubes. It is located in the SNOLAB underground facility at a depth of approximately 2 km to reduce the cosmic-ray muon induced neutron backgrounds and has been taking data stably since November 2016. DEAP-3600 demonstrated excellent performance for pulseshape discrimination between nuclear recoils and electronic recoils induced by $\beta$ and $\gamma$-rays originating from internal and external radioactivity in the detector material. It has achieved the most sensitive limit for the spin-independent WIMP-nucleon cross-section above 30 GeV/c$^{2}$ WIMP mass among argon-based experiments and leading sensitivity among all experiments for various dark matter scenarios. The talk presents the latest DEAP-3600 results demonstrating the background rejection, sensitivity to dark matter and the status of ongoing analyses.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".