Bibliographic record
Abstract
Leading cosmological surveys and models provide strong indications for cold Dark Matter (DM) being one of the major constituents of our Universe. However, direct experimental observation of the hypothesized galactic flux of DM particles streaming through the Earth remains an open quest. Following up on the successful operations at Stanford and Soudan, the SuperCDMS collaboration is currently constructing a generation-2 direct DM search experiment at the SNOLAB underground facility in Sudbury, Canada. The experiment will employ two types of cryogenic Ge and Si detectors capable of detecting sub-keV energy depositions. The unique mix of target substrates and detector technologies allows for a simultaneous study of intrinsic and external backgrounds as well as exploring the DM mass range below 10 GeV/$c^2$ with world-leading sensitivity. The two detector types are referred to as high voltage (HV) and interleaved Z-dependent ionization and phonon (iZIP) detectors. While the iZIP detectors are able to measure both phonon and ionization signals, which makes it possible to discriminate between nuclear and electronic recoils and to characterize backgrounds, the HV detectors solely measure the phonon signal. By applying a bias voltage on the order of 100 V, the primary ionization signal gets amplified in form of secondary phonons through the Neganov-Trofimov-Luke (NTL) effect yielding a lower energy threshold and excellent energy resolution for low-mass DM searches. In order to extend the sensitivity to lower energy deposition thresholds, a precise understanding of the detector response down to the semiconductor bandgap energy of $\mathcal{O}$(eV) is required. This effort is driven by a comprehensive detector testing program of SuperCDMS prototype devices at various test facilities and the development of a sophisticated Detector Monte-Carlo to guide the data analysis and model building. The current status and prospects towards science operation with SuperCDMS at SNOLAB will be reviewed in this article.
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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 | 0.016 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".