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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".