Classification of high energy muon bundles and single muons from the southern sky in IceCube
Bibliographic record
Abstract
The IceCube Neutrino Observatory, located at the geographic South Pole, uses the glacial ice volume to detect astrophysical neutrinos. Detection of the neutrinos from the northern sky provides the opportunity to use a large effective volume. However, as the cross-section increases with energy, most high-energy neutrinos are absorbed by the Earth. On the other hand, probing down-going PeV neutrinos from the southern sky becomes challenging because of the large cosmic ray induced muon backgrounds. This contribution presents a method for classifying atmospheric muon bundles and single muons by analyzing the lateral and longitudinal characteristics of throughgoing track-like events from the southern sky. Muons generated in cosmic ray air showers form muon bundles, exhibiting a lateral spread spanning tens of meters within IceCube. We explore the time residual feature for the observed Cherenkov light to separate bundles from single muons. We also utilize energy losses along the track, which lead to fluctuations in the light intensity: bundles follow a smooth pattern, whereas single muons are more stochastic. A Boosted Decision Tree algorithm is trained on simulated, well-reconstructed cosmic ray and neutrino events to classifynneutrino-induced single muons and cosmic ray bundles.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".