A Strategy for Low-Mass Dark Matter Searches with Cryogenic Detectors in the SuperCDMS SNOLAB Facility
Bibliographic record
Abstract
The SuperCDMS Collaboration is currently building SuperCDMS SNOLAB, a dark matter search focused on nucleon-coupled dark matter in the 1-5 GeV/c$^2$ mass range. Looking to the future, the Collaboration has developed a set of experience-based upgrade scenarios, as well as novel directions, to extend the search for dark matter using the SuperCDMS technology in the SNOLAB facility. The experienced-based scenarios are forecasted to probe many square decades of unexplored dark matter parameter space below 5 GeV/c$^2$, covering over 6 decades in mass: 1-100 eV/c$^2$ for dark photons and axion-like particles, 1-100 MeV/c$^2$ for dark-photon-coupled light dark matter, and 0.05-5 GeV/c$^2$ for nucleon-coupled dark matter. They will reach the neutrino fog in the 0.5-5 GeV/c$^2$ mass range and test a variety of benchmark models and sharp targets. The novel directions involve greater departures from current SuperCDMS technology but promise even greater reach in the long run, and their development must begin now for them to be available in a timely fashion. The experienced-based upgrade scenarios rely mainly on dramatic improvements in detector performance based on demonstrated scaling laws and reasonable extrapolations of current performance. Importantly, these improvements in detector performance obviate significant reductions in background levels beyond current expectations for the SuperCDMS SNOLAB experiment. Given that the dominant limiting backgrounds for SuperCDMS SNOLAB are cosmogenically created radioisotopes in the detectors, likely amenable only to isotopic purification and an underground detector life-cycle from before crystal growth to detector testing, the potential cost and time savings are enormous and the necessary improvements much easier to prototype.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".