COSINUS model-independent sensitivity to the DAMA/LIBRA dark matter signal
Bibliographic record
Abstract
Abstract COSINUS is a dark matter direct detection experiment using NaI crystals as cryogenic scintillating calorimeters. If no signal is observed, this will constrain the dark matter scattering rate in sodium iodide. We investigate how this constraint can be used to infer that the annual modulation signal observed in the DAMA/LIBRA experiment cannot originate from dark matter nuclear recoil events, independently of the dark matter model. We achieve this by unfolding the DAMA modulation spectrum to obtain the implied unquenched nuclear recoil spectrum, which we then compare to the expected COSINUS sensitivity. We find that assuming zero background in the signal region, a 1σ, 2σ or 3σ confidence limit exclusion can be obtained with 57, 130 or 250 kg day of exposure, respectively. A simple background model indicates that in the presence of background, the exposure requirements may increase by ∼ 30%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".