Low-latency neutrino follow-up combining diverse IceCube selections
Bibliographic record
Abstract
Neutrino observations are a crucial component of multi-messenger astronomy, but are currently limited by effective area and high atmospheric background. However, while other telescopes with limited field of view must be pointed in order to capture observations, IceCube’s full-sky field of view and high uptime make it an excellent instrument for realtime follow-up of astrophysical transient sources. IceCube searches for neutrino transients using an unbinned maximum likelihood method with parameters for the source’s emission time period, extension, and energy spectrum. This Fast Response Analysis can provide analysis results within tens of minutes of an astrophysical transient. Besides the follow-up of astrophysical transients manually selected as candidates, it also routinely scans areas of the sky compatible with gravitational wave alerts from LIGO/Virgo/KAGRA and IceCube event singlets which have a high probability of originating from an astrophysical source. Currently the analysis uses TeV muon neutrino candidate events whose track signature is especially suited for a precise angular reconstruction, selected and reconstructed at the South Pole and transmitted with low-latency over a satellite connection. Recently, IceCube and the neutrino astronomy community are evolving to use event samples constructed with different selections. These efforts include the follow-up of gravitational wave events with GeV neutrinos detected by IceCube-DeepCore and the observation of the Galactic plane with cascade events produced by all neutrino flavors. With plans to make IceCube-DeepCore GeV neutrino candidates and cascade events available on a day-scale latency, they can also be used in Fast Response Analyses. Moreso, multiple event samples can be combined in a Fast Response Analysis that is sensitive to a broader energy range of a neutrino transient spectrum and ensures the inclusion of all neutrino flavors. We present the analysis method and technical aspects of such an extension of the existing framework. This includes a proposed new pipeline allowing the inclusion of the more computationally-intensive reconstruction methods used by the aforementioned event selections. The extension is validated using example analyses implemented in this framework.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".