SEARCH FOR TIME-INDEPENDENT NEUTRINO EMISSION FROM ASTROPHYSICAL SOURCES WITH 3 yr OF IceCube DATA
Bibliographic record
Abstract
We present the results of a search for neutrino point sources using the IceCube data collected between 2008 April and 2011 May with three partially completed configurations of the detector: the 40-, 59-, and 79-string configurations. The live-time of this data set is 1040 days. An unbinned maximum likelihood ratio test was used to search for an excess of neutrinos above the atmospheric background at any given direction in the sky. By adding two more years of data with improved event selection and reconstruction techniques, the sensitivity was improved by a factor of 3.5 or more with respect to the previously published results obtained with the 40-string configuration of IceCube. We performed an all-sky survey and a dedicated search using a catalog of a priori selected objects observed by other telescopes. In both searches, the data are compatible with the background-only hypothesis. In the absence of evidence for a signal, we set upper limits on the flux of muon neutrinos. For an E –2 neutrino spectrum, the observed limits are (0.9-5) × 10–12 TeV–1 cm–2 s–1 for energies between 1 TeV and 1 PeV in the northern sky and (0.9-23.2) × 10–12 TeV–1 cm–2 s–1 for energies between 102 TeV and 102 PeV in the southern sky. We also report upper limits for neutrino emission from groups of sources that were selected according to theoretical models or observational parameters and analyzed with a stacking approach. Some of the limits presented already reach the level necessary to quantitatively test current models of neutrino emission.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".