Search for GeV-PeV neutrinos from nova T Coronae Borealis with IceCube
Bibliographic record
Abstract
The widely anticipated outburst of recurrent nova T Coronae Borealis (T CrB), which is near the end of its 80-year cycle, provides an excellent opportunity to search for neutrinos from novae. Novae are an energetic class of transients, which have been studied for hundreds of years. Because many of them are located nearby, novae provide an excellent astrophysical laboratory to study shock-powered emission in our own backyard. Several recent novae have previously been detected in GeV gamma rays, and the 2021 outburst of RS Ophiuchi was detected up to TeV energies, with evidence for a hadronic origin of the observed emission. Previous searches for GeV-TeV neutrinos from novae, predicted to occur alongside their gamma-ray emission, have been performed using data from the IceCube Neutrino Observatory. However, no significant neutrino signals from novae have yet been observed. We present plans for follow-up of T CrB in real time with IceCube, using datasets spanning GeV to PeV neutrino energies. Due to its closer distance and higher optical flux, which has been well measured in two historical eruptions, the expected neutrino signal from T CrB is several times stronger than that from RS Ophiuchi. Furthermore, T CrB is located in the Northern sky at a declination where IceCube’s sensitivity is an additional factor of a few better than at the location of RS Ophiuchi, which is beneficial to this search.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".