CUTE - A Cryogenic Underground Test Facility at SNOLAB
Bibliographic record
Abstract
Abstract The excellent energy resolution and low threshold of cryogenic detectors have brought them to the forefront of the search for low-mass Weakly Interacting Massive Particles. The next generation of large cryogenic detectors for dark matter search promises further improvements in sensitivity, yet it is difficult and in some cases impossible to test and fully characterize these detectors in an unshielded environment. Therefore, the Queen’s SuperCDMS team is installing a well shielded Cryogenic Underground detector TEst facility (CUTE) at SNOLAB to support detector testing and characterization for SuperCDMS and future cryogenic rare event search experiments. Significant effort is put into achieving a very low background environment which may open the door for early science results with the first set of SuperCDMS detectors during the time the main experimental apparatus is being installed. We discuss some of the challenges and solutions implemented in the design of this facility as well as the status and schedule for the start of operations underground at SNOLAB.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".