Search for High-Energy Neutrinos from Infrared Flares
Bibliographic record
Abstract
IceCube has detected a diffuse flux of high-energy neutrinos, whose origin still remains uncertain. Accreting supermassive black holes (SMBHs) have been proposed as plausible sources of neutrinos. Candidate sources include AT2019dsg, which is likely a stellar tidal disruption event (TDE), and AT2019dfr, an AGN flare. Both present delayed emission in the IR band with respect to the optical signal. This emission can be interpreted as the reprocessing of X-rays to optical light of the flare by dust located in a torus around the SMBH. An additional study using an optically detected sample of 63 accretion flares revealed another candidate as a potential high-energy neutrino counterpart: AT2019aalc, which is also accompanied by a dust echo. However, follow-up stacking analysis of the 63 nuclear flares using the full IceCube data sample did not show any significant excess over background. Motivated by these three suggested neutrino-TDE correlations, we analyse a more extensive catalog of IR flares, 823 dust-echo-like flares identified using WISE satellite data, against the IceCube 10-year sample of track events from the Northern Sky. Our analysis aims to perform sensitivity studies and assess the potential detectability of neutrino emission from these types of accretion flares. In addition, we carry out a correlation study of the 823 dust echo-like flares against a revised catalog of IceCube high-purity astrophysical alerts, and reevaluate the previous study of 63 nuclear flares against the same revised alerts sample.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".