Search for a diffuse flux of astrophysical muon neutrinos with the IceCube 40-string detector
Bibliographic record
Abstract
The IceCube Neutrino Observatory is a $1\text{ }\text{ }{\mathrm{km}}^{3}$ detector currently taking data at the South Pole. One of the main strategies used to look for astrophysical neutrinos with IceCube is the search for a diffuse flux of high-energy neutrinos from unresolved sources. A hard energy spectrum of neutrinos from isotropically distributed astrophysical sources could manifest itself as a detectable signal that may be differentiated from the atmospheric neutrino background by spectral measurement. This analysis uses data from the IceCube detector collected in its half completed configuration which operated between April 2008 and May 2009 to search for a diffuse flux of astrophysical muon neutrinos. A total of 12 877 upward-going candidate neutrino events have been selected for this analysis. No evidence for a diffuse flux of astrophysical muon neutrinos was found in the data set leading to a 90% C.L. upper limit on the normalization of an ${E}^{\ensuremath{-}2}$ astrophysical ${\ensuremath{\nu}}_{\ensuremath{\mu}}$ flux of $8.9\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}\text{ }\text{ }\mathrm{GeV}\text{ }{\mathrm{cm}}^{\ensuremath{-}2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{sr}}^{\ensuremath{-}1}$. The analysis is sensitive in the energy range between 35 TeV and 7 PeV. The 12 877 candidate neutrino events are consistent with atmospheric muon neutrinos measured from 332 GeV to 84 TeV and no evidence for a prompt component to the atmospheric neutrino spectrum is found.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".