A New View on Auger Data and Cosmogenic Neutrinos in Light of Different Nuclear Disintegration and Air-shower Models
Bibliographic record
Abstract
We study the implications of Ultra-High Energy Cosmic Ray (UHECR) data from the Pierre Auger Observatory for potential accelerator candidates and cosmogenic neutrino fluxes for combinations of nuclear disintegration and air-shower models. We exploit the most recently published spectral and mass composition data (2017) with a new, computationally efficient simulation code PriNCe. We extend a systematic framework, which has been previously applied in a combined fit by the Pierre Auger Collaboration, with the cosmological source evolution as an additional free parameter. In this framework, an ensemble of generalized UHECR accelerators is characterized by a universal spectral index (equal for all injection species), a maximal rigidity, and the normalizations for five nuclear element groups. We find that the 2017 data favor a small but constrained contribution of heavy elements (iron) at the source. We demonstrate that the results moderately depend on the nuclear disintegration (PSB, Peanut, or Talys) model, and more strongly on the air-shower (EPOS-LHC, Sibyll-2.3, or QGSjet-II-04) model. Variations of these models result in different source evolutions and spectral indices, limiting the interpretation in terms of a particular class of cosmic accelerators. Better constrained parameters include the maximal rigidity and the mass composition at the source. Hence, the cosmogenic neutrino flux can be robustly predicted. Depending on the source evolution at high redshifts the flux is likely out of reach of future neutrino observatories in most cases, and a minimal cosmogenic neutrino flux cannot be claimed from data without assuming a cosmological distribution of the sources
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".