Measuring the galactic plane and searching for galactic PeVatrons using the IceCube Multi-Flavor Astrophysical Neutrino sample
Bibliographic record
Abstract
The IceCube Neutrino Observatory has provided new insights into the high-energy universe, in particular, unveiling neutrinos from the galactic plane. However, galactic neutrino sources are still unresolved. The recent detection of multi-PeV photons by LHAASO from the Cygnus region highlights its potential as a galactic neutrino source. Additionally, LHAASO, HAWC, and HESS have reported over forty galactic gamma-ray sources with energies above 100 TeV. Detecting neutrinos correlated with high-energy gamma-ray sources would provide compelling evidence of cosmic-ray acceleration in these galactic sources. In this work, we compile a 12.3-year, full-sky, all-flavor dataset, the IceCube Multi-Flavor Astrophysics Neutrino sample (ICEMAN). ICEMAN is the combination of three largely independent neutrino samples of different event morphologies and builds upon the previous work of the DNN-based cascade sample, Enhanced Starting Track Event Selection, and the Northern Track sample. Recent improvements in ice modeling and detector calibration are also incorporated into the cascade reconstruction. In addition to revisiting the galactic plane, we adopt two different analysis methods to search for galactic PeVatrons. First, we use a template-based approach to probe the Cygnus Cocoon region. Second, we use a point source hypothesis to find correlations between IceCube neutrinos and gamma-ray sources detected at energies greater than 100 TeV.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".