Search for steady point-like sources in the astrophysical muon neutrino flux with 8 years of IceCube data
Bibliographic record
Abstract
The IceCube Collaboration has observed a high-energy astrophysical neutrino flux and recently found evidence for neutrino emission from the blazar TXS 0506 $$+$$ 056. These results open a new window into the high-energy universe. However, the source or sources of most of the observed flux of astrophysical neutrinos remains uncertain. Here, a search for steady point-like neutrino sources is performed using an unbinned likelihood analysis. The method searches for a spatial accumulation of muon-neutrino events using the very high-statistics sample of about 497,000 neutrinos recorded by IceCube between 2009 and 2017. The median angular resolution is $$\sim 1^\circ $$ at 1 TeV and improves to $$\sim 0.3^\circ $$ for neutrinos with an energy of 1 PeV. Compared to previous analyses, this search is optimized for point-like neutrino emission with the same flux-characteristics as the observed astrophysical muon-neutrino flux and introduces an improved event-reconstruction and parametrization of the background. The result is an improvement in sensitivity to the muon-neutrino flux compared to the previous analysis of $$\sim 35\%$$ assuming an $$E^{-2}$$ spectrum. The sensitivity on the muon-neutrino flux is at a level of $$E^2 \mathrm {d} N /\mathrm {d} E = 3\cdot 10^{-13}\,\mathrm {TeV}\,\mathrm {cm}^{-2}\,\mathrm {s}^{-1}$$ . No new evidence for neutrino sources is found in a full sky scan and in an a priori candidate source list that is motivated by gamma-ray observations. Furthermore, no significant excesses above background are found from populations of sub-threshold sources. The implications of the non-observation for potential source classes are 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".