Probing the cosmic ray background of gamma-ray astronomy with hadron colliders
Bibliographic record
Abstract
Hadronic cosmic particles (cosmic rays) and gamma rays are constantly absorbed in the Earth’s atmosphere and result in air showers of secondary particles. Cherenkov radiation from these atmospheric events is used to measure cosmic gamma rays with ground-based telescopes. We focus here on the dominant hadronic cosmic-ray-initiated background events in the atmosphere, which give rise to gamma-ray-like air showers for gamma-ray telescopes. It is shown that only a small subset of hadronic cosmic-ray interactions, those which produce a large energy neutral pion, are responsible for this background. We subsequently address how the predictions of this background vary depending on the hadronic interaction model adopted. The pseudorapidity range of the energetic pions, with respect to the shower axis produced in these background events, is shown to be large. We show that collider experiments, specifically LHCf and the Relavistic Heavy Ion Collider (RHICf), probe cosmic ray interactions precisely within this pseudorapidity range. Present and future measurements with these instruments are shown to be able to test the ability for current hadronic interaction models to accurately describe these background events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".