A global fit of non-relativistic effective dark matter operators including solar neutrinos
Bibliographic record
Abstract
Abstract We perform a global fit of dark matter interactions with nucleons using a non-relativistic effective operator description, considering both direct detection and neutrino data. We examine the impact of combining the direct detection experiments CDMSlite, CRESST-II, CRESST-III, DarkSide-50, DarkSide-50-S2-Only, LUX, LZ, PandaX-II, PandaX-4T, PICO-2L, PICO-60, SIMPLE, XENON100, and XENON1T along with neutrino data from IceCube, ANTARES, DeepCore, and Super-Kamiokande. While current neutrino telescope data lead to increased sensitivity compared to underground nuclear scattering experiments for dark matter masses above 100 GeV, our future projections show that the next generation of underground experiments will significantly outpace solar searches for most dark matter-nucleon elastic scattering interactions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".