Measurement of the Atmospheric<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>ν</mml:mi><mml:mi>e</mml:mi></mml:msub></mml:math>Spectrum with IceCube
Bibliographic record
Abstract
We present a measurement of the atmospheric ${\ensuremath{\nu}}_{e}$ spectrum at energies between 0.1 and 100 TeV using data from the first year of the complete IceCube detector. Atmospheric ${\ensuremath{\nu}}_{e}$ originate mainly from the decays of kaons produced in cosmic-ray air showers. This analysis selects 1078 fully contained events in 332 days of live time, and then identifies those consistent with particle showers. A likelihood analysis with improved event selection extends our previous measurement of the conventional ${\ensuremath{\nu}}_{e}$ fluxes to higher energies. The data constrain the conventional ${\ensuremath{\nu}}_{e}$ flux to be $1.{3}_{\ensuremath{-}0.3}^{+0.4}$ times a baseline prediction from a Honda's calculation, including the knee of the cosmic-ray spectrum. A fit to the kaon contribution ($\ensuremath{\xi}$) to the neutrino flux finds a kaon component that is $\ensuremath{\xi}=1.{3}_{\ensuremath{-}0.4}^{+0.5}$ times the baseline value. The fitted/measured prompt neutrino flux from charmed hadron decays strongly depends on the assumed astrophysical flux and shape. If the astrophysical component follows a power law, the result for the prompt flux is $0.{0}_{\ensuremath{-}0.0}^{+3.0}$ times a calculated flux based on the work by Enberg, Reno, and Sarcevic.
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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) | 0.003 | 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".