Accelerating Earth-bound dark matter
Bibliographic record
Abstract
A fraction of the dark matter may consist of a particle species that interacts much more strongly with the Standard Model than a typical weakly interacting massive particle (WIMP) of similar mass. Such a strongly interacting dark matter component could have avoided detection in searches for WIMP-like dark matter through its interactions with the material in the atmosphere and the Earth that slow it down significantly before reaching detectors underground. These same interactions can also enhance the density of a strongly interacting dark matter species near the Earth's surface to well above the local galactic dark matter density. In this work, we propose two new methods of detecting strongly interacting dark matter based on accelerating the enhanced population expected in the Earth through scattering. The first approach is to use underground nuclear accelerator beams to upscatter the ambient dark matter population into a WIMP-style detector located downstream. In the second technique, dark matter is upscattered with an intense thermal source and detected with a low-threshold dark matter detector. We also discuss potential candidates for strongly interacting dark matter, and we show that the scenario can be naturally realized with a hidden fermion coupled to a sub-GeV dark photon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".