First Direct Detection Constraints on Planck-Scale Mass Dark Matter with Multiple-Scatter Signatures Using the DEAP-3600 Detector
Bibliographic record
Abstract
Dark matter with Planck-scale mass ($\ensuremath{\simeq}{10}^{19}\text{ }\text{ }\mathrm{GeV}/{c}^{2}$) arises in well-motivated theories and could be produced by several cosmological mechanisms. A search for multiscatter signals from supermassive dark matter was performed with a blind analysis of data collected over a 813 d live time with DEAP-3600, a 3.3 t single-phase liquid argon-based detector at SNOLAB. No candidate signals were observed, leading to the first direct detection constraints on Planck-scale mass dark matter. Leading limits constrain dark matter masses between $8.3\ifmmode\times\else\texttimes\fi{}{10}^{6}$ and $1.2\ifmmode\times\else\texttimes\fi{}{10}^{19}\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, and $^{40}\mathrm{Ar}$-scattering cross sections between $1.0\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}23}$ and $2.4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}18}\text{ }\text{ }{\mathrm{cm}}^{2}$. These results are interpreted as constraints on composite dark matter models with two different nucleon-to-nuclear cross section scalings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".