CUTE: A Cryogenic Underground TEst facility at SNOLAB
Bibliographic record
Abstract
Low-temperature cryogenics open the door for a range of interesting technologies based on features like superconductivity and superfluidity, low-temperature phase transitions or the low heat capacity of non-metals in the milli-Kelvin range. Devices based on these technologies are often sensitive to small energy depositions as can be caused by environmental radiation. The Cryogenic Underground TEst facility (CUTE) at SNOLAB is a platform for testing and operating cryogenic devices in an environment with low levels of background. The large experimental chamber ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mi mathvariant="script">O</mml:mi></mml:math> (10) L) reaches a base temperature of ∼ 12 mK; it can hold a payload of up to ∼ 20 kg and provides a radiogenic background event rate as low as a few events/kg/keV/day in the energy range below about 100 keV, as well as a negligible muon rate ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mi mathvariant="script">O</mml:mi></mml:math> 1)/month). CUTE was designed and built in the context of the Super Cryogenic Dark Matter Search experiment (SuperCDMS) that uses cryogenic detectors to search for interactions of dark matter particles with ordinary matter. The facility has been used to test SuperCDMS detectors since its commissioning in 2019. In 2021, it was handed over to SNOLAB to become a SNOLAB user facility after the completion of the testing of detectors for SuperCDMS. The facility will be available for projects that benefit from these special conditions, based on proposals assessed for their scientific and technological merits. This article describes the main design features and operating parameters of CUTE.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.057 | 0.023 |
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".