Refining the sensitivity of new physics searches with ancient minerals
Bibliographic record
Abstract
Paleodetection has been proposed as a competitive method for detecting dark matter and other new physics interactions, complementing conventional direct-detection experiments. In this work, we utilize simulations to improve the modeling of track-length distributions. Our findings suggest that previous studies have overestimated the number of tracks caused by weakly interacting particles, and that the lowest observable dark matter mass should be higher than previously predicted. These differences are mainly attributed to the fact that (a) the recoil energy–track-length relation is not one to one, (b) at low recoil energies, a substantial fraction of recoils do not yield any tracks, and (c) at high energies, electronic stopping becomes dominant, resulting in a track-length barrier at ∼ 200 nm . In addition to WIMPs, we also modeled tracks from generalized coherent elastic neutrino nucleus scattering ( CE ν NS ) via new light mediators and estimated the projected sensitivity for these 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".