Investigating the sources of low-energy events in a SuperCDMS-HVeV detector
Bibliographic record
Abstract
Recent experiments searching for $\mathrm{sub}\text{\ensuremath{-}}\mathrm{GeV}/{c}^{2}$ dark matter have observed event excesses close to their respective energy thresholds. Although specific to the individual technologies, the measured excess event rates have been consistently reported at or below event energies of a few-hundred eV, or with charges of a few electron-hole pairs. In the present work, we operated a 1-g silicon SuperCDMS-HVeV detector at three voltages across the crystal (0, 60 and 100 V). The 0 V data show an excess of events in the tens of eV region. Despite this event excess, we demonstrate the ability to set a competitive exclusion limit on the spin-independent dark matter--nucleon elastic scattering cross section for dark matter masses of $\mathcal{O}(100)\text{ }\text{ }\mathrm{MeV}/{c}^{2}$, enabled by operation of the detector at 0 V potential and achievement of a very low $\mathcal{O}(10)\text{ }\text{ }\mathrm{eV}$ threshold for nuclear recoils. Comparing the data acquired at 0, 60 and 100 V potentials across the crystal, we investigated possible sources of the unexpected events observed at low energy. The data indicate that the dominant contribution to the excess is consistent with a hypothesized luminescence from the printed circuit boards used in the detector holder.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".