Improved photomeson model for interactions of cosmic ray nuclei
Bibliographic record
Abstract
Photohadronic interactions are important for the sources and the transport of Ultra-High Energy Cosmic Rays (UHECRs). Current state-of-the-art cosmic ray transport simulations handle nuclear disintegration at energies of the Giant Dipole Resonance at a more sophisticated level, as well as the photohadronic interactions of nucleons in the high-energy regime above the pion production threshold. However, the interactions of nuclei above the pion production threshold are commonly modeled by treating the nucleus as a superposition of free nucleons—ignoring the effect of the nuclear medium. We construct an improved, inclusive model for the photomeson regime for nuclei with A ⩽ 56 by employing more accurate, data-driven parametrizations of the interaction cross section, the fragmentation of the primary nucleus and the inclusive pion production cross section that directly affects the production of astrophysical neutrinos. We apply our results to two multi-messenger scenarios (Tidal Disruption Events and Gamma-Ray Bursts) in which photonuclear interactions in the photomeson regime are the dominant cooling process for the highest energy cosmic rays. While we find moderate changes to the mass composition of UHECRs, the astrophysical neutrino fluxes exhibit a significant (factor of a few) reduction compared to the naive superposition of free nucleons for sources of UHECR nuclei with a populated cascade. The numerical code implementing the model has been made publicly available, which facilitates the integration of our results in similar frameworks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".