Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector
Bibliographic record
Abstract
DEAP-3600 is a single-phase liquid argon detector aiming to directly detect weakly interacting massive particles (WIMPs), located at SNOLAB (Sudbury, Canada). After analyzing data taken during the first year of operation, a null result was used to place an upper bound on the WIMP-nucleon, spin-independent, isoscalar cross section. This study reinterprets this result within a nonrelativistic effective field theory framework and further examines how various possible substructures in the local dark matter halo may affect these constraints. Such substructures are hinted at by kinematic structures in the local stellar distribution observed by the Gaia satellite and other recent astronomical surveys. These include the Gaia Sausage (or Enceladus), as well as a number of distinct streams identified in recent studies. Limits are presented for the coupling strength of the effective contact interaction operators ${\mathcal{O}}_{1}$, ${\mathcal{O}}_{3}$, ${\mathcal{O}}_{5}$, ${\mathcal{O}}_{8}$, and ${\mathcal{O}}_{11}$, considering isoscalar, isovector, and xenonphobic scenarios, as well as the specific operators corresponding to millicharge, magnetic dipole, electric dipole, and anapole interactions. The effects of halo substructures on each of these operators are explored as well, showing that the ${\mathcal{O}}_{5}$ and ${\mathcal{O}}_{8}$ operators are particularly sensitive to the velocity distribution, even at dark matter masses above $100\text{ }\text{ }\mathrm{GeV}/{c}^{2}$.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".