Improvements in the Reconstruction of IceCube Realtime Alerts
Bibliographic record
Abstract
In 2016, the IceCube Neutrino Observatory launched its realtime program. When a neutrino candidate of likely astrophysical origin is detected, a public alert is issued, typically within one minute. These alerts allow the astrophysical community to follow up on the region of the sky where the neutrino likely originated. Initially, the system issued around six track-signature alerts per year, with a highlight being IceCube-170922A, which was later associated with the flaring blazar TXS 0506+056. Since 2019, IceCube has expanded the selection criteria for neutrino candidates, increasing the track alert rate to around 30 per year with additional alerts for cascade signatures of probable astrophysical origin implemented in 2020. This work describes several improvements in the reconstruction of track alerts, which were introduced into the realtime stream in 2024, and details the two algorithms that are alternately used for reconstruction, depending on the reconstructed muon energy. The improvements result in more precise directional localizations, with a factor of 5 (4) reduction in the 50% (90%) contour area. Systematic errors affecting the reconstructions, such as the ice model or the geometry of the detector, have also been investigated to ensure statistical coverage. In addition to its application in the realtime stream, the improvements described here are also being applied to historical alerts with an updated catalog of track alerts forthcoming.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".