daemonflux: Data-Driven Muon-Calibrated Neutrino Flux
Bibliographic record
Abstract
This study presents a refined calculation of the atmospheric neutrino flux from GeV to PeV energies based on two data-driven models for the flux of cosmic ray nucleons (GSF6) and hadronic interactions (DDM, previously detailed in Fedynitch & Huber, PRD106 083018, 2022). The uncertainties are treated as adjustable parameters and are fitted using a combination of muon spectrometer data at the surface, including constraints from fixed-target experiments. The resulting calculated neutrino fluxes have uncertainties of less than 10% up to 1 TeV and show significant differences compared to previous calculations. We discuss which experiments have the most significant impact on the fit and propose how this method can incorporate additional constraints, such as measurements of deep underground muon intensities or seasonal variations, in the future. Our model is publicly available via the daemonflux code, which provides access to all model parameters and the covariance matrix obtained from the fit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".