DEAP-3600 - Latest results from the largest liquid argon dark matter experiment
Bibliographic record
Abstract
DEAP-3600 is a multi-tonne experiment at SNOLAB, an underground laboratory located at a depth of 2 km in Sudbury, Canada. The detector is filled with approximately 3.3 tonnes of liquid argon contained in a 1.7 m diameter ultra-low-background acrylic vessel operating at temperature of ~87 K and is designed for the direct detection of Weakly Interacting Massive Particles (WIMPs), one of the most promising dark matter candidates. DEAP-3600 set world-leading constraints on TeV-scale mass dark matter searches with liquid argon as scattering target, as well as on Planck-scale mass dark matter. The detector relies on the pulseshape discrimination method, which allows the rejection of electronic recoil backgrounds with better than $10^{-10}$ leakage probability at 50% nuclear recoil acceptance above 18 keVee. In this contribution, the latest results from DEAP-3600 will be presented, including a description of the background model as well as dark matter search results.
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 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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".