A Modern High-Precision Calculation of Deep Underground Cosmic Ray Muons
Bibliographic record
Abstract
We present a new efficient calculation to propagate cosmic ray muons from the surface of the Earth to deep underground laboratories, allowing us to look at the physics and performance of various models of high-energy cosmic rays. The evolution of cosmic rays in the Earth's atmosphere is computed with MCEq (Matrix Cascade Equation), taking into account different combinations of primary and hadronic interaction models in order to calculate the muon flux at the surface. The latter serves as an input for the Monte Carlo code PROPOSAL (Propagator with Optimal Precision and Optimised Speed for All Leptons) to propagate the muons through the rock. A forward prediction for underground muon spectra at different slant depths, including the muon survival probabilities and underground energy spectra, is calculated with very high precision. The reliability of this state-of-the-art calculation was achieved by comparing the results obtained for the vertical muon intensity and total muon flux with the measured data at various underground sites with both flat overburdens and mountains. The implications of the results as well as the seasonal variation of the muon flux will also be discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".