χ<tt>aro</tt>ν: a tool for neutrino flux generation from WIMPs
Bibliographic record
Abstract
Indirect searches for signatures of corpuscular dark matter have been performed using all cosmic messengers: gamma rays, cosmic rays, and neutrinos. The search for dark matter from neutrinos is of particular importance since they are the only courier that can reach detectors from dark matter processes in dense environments, such as the core of the Sun or Earth, or from the edge of the observable Universe. In this work, we introduce χ aro ν, a software package that, in the spirit of its mythological Greek namesake χ ́α ρ ω ν, bridges the dark sector and Standard Model by predicting neutrino fluxes from different celestial dark matter agglomerations. The flux at the point of production is either computed internally by χ aro ν or is taken from user supplied tables. χ aro ν then propagates this flux through vacuum or dense media and returns the expected neutrino yield at an observer's location. In developing χ aro ν, we have revisited and updated the production of neutrinos in dense media, updated the propagation of high-energy neutrinos, and studied the sources of uncertainty in neutrino transport. This package is coupled to a new calculation that includes electroweak corrections resulting in the most up-to-date and complete repository of neutrino fluxes from dark matter decay and annihilation over the energy range of 1 GeV to 10 PeV coming from the Earth, the Sun, and the Galactic halo.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.026 |
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".