Bibliographic record
Abstract
The Super Cryogenic Dark Matter Search (SuperCDMS) experiment at SNOLAB explores dark matter candidates in the mass range of 1-10 $\mathrm{GeV/c^2}$. The experiment will deploy a total of 24 detectors with silicon and germanium substrates, 2 km deep underground in SNOLAB, Canada. The detectors are arranged in four towers, combining the low-threshold sensitivity of high-voltage (HV) phonon detectors with the excellent background discrimination of phonon-charge (iZIP)detectors. The combination will give SuperCDMS a world-leading edge in the search for low-mass, low-cross-section dark matter candidates. Over the past year, SuperCDMS completed testing a single HV tower at the Cryogenic Underground TEst facility (CUTE). The CUTE run, which concluded in February 2024, demonstrated the ability to conduct in-situ operations including characterization, calibration, and exploration of Neganov-Trofimov-Luke (NTL) phononamplification. This contribution will highlight key results from CUTE, along with the current status and prospects of the first SuperCDMS science run.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".