Measurement of Atmospheric Neutrino Oscillations with IceCube
Bibliographic record
Abstract
We present the first statistically significant detection of neutrino oscillations in the high-energy regime ($>20\text{ }\text{ }\mathrm{GeV}$) from an analysis of IceCube Neutrino Observatory data collected in 2010 and 2011. This measurement is made possible by the low-energy threshold of the DeepCore detector ($\ensuremath{\sim}20\text{ }\text{ }\mathrm{GeV}$) and benefits from the use of the IceCube detector as a veto against cosmic-ray-induced muon background. The oscillation signal was detected within a low-energy muon neutrino sample (20--100 GeV) extracted from data collected by DeepCore. A high-energy muon neutrino sample (100 GeV--10 TeV) was extracted from IceCube data to constrain systematic uncertainties. The disappearance of low-energy upward-going muon neutrinos was observed, and the nonoscillation hypothesis is rejected with more than $5\ensuremath{\sigma}$ significance. In a two-neutrino flavor formalism, our data are best described by the atmospheric neutrino oscillation parameters $|\ensuremath{\Delta}{m}_{32}^{2}|=({2.3}_{\ensuremath{-}0.5}^{+0.6})\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{eV}}^{2}$ and ${sin}^{2}(2{\ensuremath{\theta}}_{23})>0.93$, and maximum mixing is favored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".