Searching for Dark Matter with the Sudbury Neutrino Observatory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dark matter currently makes up approximately 84% of the matter in our universe, but has yet to be observed. A recent model by Grossman, Harnik, Telem, and Zhang proposes a new form of dark matter called self-destructing matter which could decay to standard model leptons after an interaction in Earth. Motivated by this model, in this analysis we perform two distinct analyses looking at high energy events in the Sudbury Neutrino Observatory data between 1999 and 2003. In the first, we perform a null hypothesis test on the data between 20 MeV and 10 GeV to look for any data which is not consistent with atmospheric neutrinos and find no evidence for new physics. In the second analysis we perform a dedicated search for back to back lepton pairs from a slow dark mediator in the self-destructing dark matter model. We find no evidence for the self-destructing dark matter and place new limits on the rate of these events.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it