Constraints on Sterile Neutrino Models from Strong Gravitational Lensing, Milky Way Satellites, and the Lyman-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>α</mml:mi></mml:math> Forest
Bibliographic record
Abstract
The nature of dark matter is one of the most important unsolved questions in science. Some darkf matter candidates do not have sufficient nongravitational interactions to be probed in laboratory or accelerator experiments. It is thus important to develop astrophysical probes which can constrain or lead to a discovery of such candidates. We illustrate this using state-of-the-art measurements of strong gravitationally lensed quasars to constrain four of the most popular sterile neutrino models, and also report the constraints for other independent methods that are comparable in procedure. First, we derive effective relations to describe the correspondence between the mass of a thermal relic warm dark matter particle and the mass of sterile neutrinos produced via Higgs decay and grand unified theory (GUT)-scale scenarios, in terms of large-scale structure and galaxy formation astrophysical effects. Second, we show that sterile neutrinos produced through the Higgs decay mechanism are allowed only for mass >26 keV, and GUT-scale scenario >5.3 keV. Third, we show that the single sterile neutrino model produced through active neutrino oscillations is allowed for mass >92 keV, and the three sterile neutrino minimal standard model (νMSM) for mass >16 keV. These are the most stringent experimental limits on these models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".