The SuperCDMS experiment: Status and prospects
Bibliographic record
Abstract
The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is one of the leading role actors in the search for Dark Matter (DM), focusing on particles with masses below 10 GeV/c2. After its successful campaign in the Soudan \nUnderground Laboratory, the project is preparing for its next phase moving to the SNOLAB laboratory in Sudbury, Canada. Improved detector technologies and the new experiment set-up will allow to push the sensitivity to lower masses, down to about 0.5 GeV/c2 for Weakly Interacting Massive Particles (WIMPs) and to improve \nthe cross-section reach by more than one order of magnitude. One key ingredient for the experiment’s success is the precise knowledge of the ionization yield in silicon (Si) and germanium (Ge) at low energy. This manuscript, after briefly describing the SuperCDMS status and prospects, reports the measurement of the ionization yield in Ge performed by the collaboration using data from the previous campaign in Soudan.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".