Constraints on Solar Axions Using the Profile Likelihood Ratio Method with the SuperCDMS Experiments
Bibliographic record
Abstract
Dark matter plays an essential role in understanding modern physics and particles beyond the Standard Model. Evidence suggests that dark matter accounts for approximately 85% of the universe’s matter, and 26.8% of its mass-energy composition. Key candidates for dark matter include unidentified subatomic particles like Weakly Interacting Massive Particles (WIMPs) and axions. The Super Cryogenic Dark Matter Search (SuperCDMS) employs direct detection methods to identify these elusive particles using cryogenic technologies. Su- perCDMS Soudan is the latest completed CDMS experiment in Minnesota, in preparation for the next phase experiments of SuperCDMS SNOLAB in Sudbury, Canada. At SNOLAB, the Cryogenic Underground TEst (CUTE) facility is dedicated to analyze background levels prior to the full operation of SuperCDMS SNOLAB experiments. Utilizing collected data from CDMSlite Run 3 at Soudan Underground Laboratory with minimized background interference, sensitivity limits were established for solar axions within the keV energy range using the profile likelihood ratio method. Our results show an axio-electric coupling constant constraints g_ae < 5.91×10^–11 from the atomic recombination and de-excitation, Bremsstrahlung, and Compton channels at a 90% confidence level.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".