Constraints on Lightly Ionizing Particles from CDMSlite
Bibliographic record
Abstract
The Cryogenic Dark Matter Search low ionization threshold experiment (CDMSlite) achieved efficient detection of very small recoil energies in its germanium target, resulting in sensitivity to lightly ionizing particles (LIPs) in a previously unexplored region of charge, mass, and velocity parameter space. We report first direct-detection limits calculated using the optimum interval method on the vertical intensity of cosmogenically produced LIPs with an electric charge smaller than $e/(3\ifmmode\times\else\texttimes\fi{}{10}^{5}$), as well as the strongest limits for charge $\ensuremath{\le}e/160$, with a minimum vertical intensity of $1.36\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}\text{ }\text{ }{\mathrm{cm}}^{\ensuremath{-}2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{sr}}^{\ensuremath{-}1}$ at charge $e/160$. These results apply over a wide range of LIP masses ($5\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ to $100\text{ }\text{ }\mathrm{TeV}/{c}^{2}$) and cover a wide range of $\ensuremath{\beta}\ensuremath{\gamma}$ values ($0.1--{10}^{6}$), thus excluding nonrelativistic LIPs with $\ensuremath{\beta}\ensuremath{\gamma}$ as small as 0.1 for the first time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".