A Two-Component Lateral Distribution Function for the Reconstruction of Air-Shower Events in IceTop
Bibliographic record
Abstract
The surface component of the IceCube Neutrino Observatory, IceTop, consists of an array of ice-Cherenkov tanks measuring the electromagnetic signal as well as low-energy ($\sim\rm{GeV}$) muons from cosmic-ray air showers. In addition, accompanying high-energy (above a few $100\,\rm{GeV}$) muons can be observed in coincidence in the deep in-ice detector. A combined measurement of the low- and high-energy muon content is of particular interest for tests of hadronic interaction models as well as for cosmic-ray mass discrimination. However, since IceTop does not feature dedicated muon detectors, an estimation of the low-energy muon component of individual air showers is challenging. In this work, a two-component lateral distribution function (LDF), using separate descriptions for the electromagnetic and muon lateral distributions of the detector signals, is introduced as a new approach for the estimation of low-energy muons in air showers on an event-by-event basis. The principle of the air-shower reconstruction using the two-component LDF, as well as its reconstruction performance with respect to primary energy and number of low-energy muons will 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".