Constraints on low-mass, relic dark matter candidates from a surface-operated SuperCDMS single-charge sensitive detector
Bibliographic record
Abstract
This article presents an analysis and the resulting limits on light dark matter inelastically scattering off of electrons, and on dark photon and axionlike particle absorption, using a second-generation SuperCDMS high-voltage eV-resolution detector. The 0.93 g Si detector achieved a 3 eV phonon energy resolution; for a detector bias of 100 V, this corresponds to a charge resolution of 3% of a single electron-hole pair. The energy spectrum is reported from a blind analysis with 1.2 g-days of exposure acquired in an above-ground laboratory. With charge carrier trapping and impact ionization effects incorporated into the dark matter signal models, the dark matter-electron cross section ${\overline{\ensuremath{\sigma}}}_{e}$ is constrained for dark matter masses from 0.5 to ${10}^{4}\text{ }\text{ }\mathrm{MeV}/{c}^{2}$; in the mass range from 1.2 to $50\text{ }\text{ }\mathrm{eV}/{c}^{2}$ the dark photon kinetic mixing parameter $ϵ$ and the axioelectric coupling constant ${g}_{ae}$ are constrained. The minimum 90% confidence-level upper limits within the above-mentioned mass ranges are ${\overline{\ensuremath{\sigma}}}_{e}=8.7\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}34}\text{ }\text{ }{\mathrm{cm}}^{2}$, $ϵ=3.3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}14}$, and ${g}_{ae}=1.0\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}$.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".