Challenges and Opportunities for Predicting Muons in Underground and Underwater Labs Using MUTE
Bibliographic record
Abstract
MUTE (MUon inTensity codE) is a Python program (https://github.com/wjwoodley/mute) that combines two state-of-the-art codes, MCEq and PROPOSAL, to predict muon intensities and spectra in underground and underwater laboratories. Our previous work (A. Fedynitch, W. Woodley, M.-C. Piro 2022 ApJ 928 27) has demonstrated the accuracy of MUTE in reproducing the measured vertical equivalent muon intensities within the models' uncertainties. Moreover, we have shown that the experimental uncertainties are smaller than the theoretical uncertainties, making the vertical-equivalent data an effective calibration source for high-energy neutrino flux calculations. In this new study, we expand our analysis by calculating the total muon intensities and seasonal variations in labs located under flat earth and mountains using topographic maps of the overburdens. While our model predicts the amplitude of seasonal variations well, we identified inconsistencies amongst measurements at several labs, which pose additional challenges for interpretation. Additionally, the uncertainty in the rock density above many labs is a significant source of systematic uncertainty in total muon intensity measurements. Although MUTE accurately describes this data, we found that the uncertainties of the data were similar to our nominal prediction. We also present calculations using the daemonflux model, a muon and neutrino flux model calibrated with muon measurements at the surface, and using constraints from near-horizontal measurements at the highest energies. We examine whether the near-horizontal data is consistent with the underground 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".