Testing Hadronic Interaction Models with Cosmic Ray Measurements at the IceCube Neutrino Observatory
Bibliographic record
Abstract
The IceCube Neutrino Observatory provides the opportunity to perform unique measurements of cosmic-ray air showers with its combination of a surface array and a deep detector. Electromagnetic particles and low-energy muons (∼GeV) are detected by IceTop, while a bundle of high-energy muons (>~400 GeV) can be measured in coincidence in IceCube. Predictions of air-shower observables based on simulations show a strong dependence on the choice of the high-energy hadronic interaction model. By reconstructing different composition-dependent observables, one can provide strong tests of hadronic interaction models, as these measurements should be consistent with one another. In this work, we present an analysis of air-shower data between 2.5 and 80 PeV, comparing the composition interpretation of measurements of the surface muon density, the slope of the IceTop lateral distribution function, and the energy loss of the muon bundle, using the models Sibyll 2.1, QGSJet-II.04 and EPOS-LHC. We observe inconsistencies in all models under consideration, suggesting they do not give an adequate description of experimental data. The results furthermore imply a significant uncertainty in the determination of the cosmic-ray mass composition through indirect measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".