The physical properties of candidate neutrino-emitter blazars
Bibliographic record
Abstract
Context. The processes governing the production of astrophysical high-energy neutrinos are still a matter of debate, and the sources that originate them remain an open question. Among the putative emitters, active galactic nuclei (AGN) have gained increasing attention in recent years. Blazars, in particular, stand out due to their capability of accelerating particles in environments with external radiation fields. Recent observations suggest that they may play a role in the production of high-energy neutrinos detected by the IceCube observatory. Aims. We studied the physical properties of a subsample of 52 blazars, that have been proposed as candidate neutrino emitters, based on a positional cross-correlation statistical analysis between IceCube hotspots and the Fifth Edition of the Roma BZCat catalog. We provide a first characterization of their central engines and inner physical nature, which may help to explore the potential link with neutrino production. Methods. This study carries out an analysis of the optical spectroscopic properties of a sample of 52 candidate neutrino-emitter blazars, to infer their accretion regime. It is complemented by data at the radio and γ-ray frequencies, which carry the information about the intrinsic power of the relativistic jet. We compared the properties of the sample of candidate neutrino-emitter blazars to other blazar samples from the literature. To this end, we performed statistical tests and also explored, through simulations, the applicability of methods that include limits (censored data) on the quantities of our interest. Results. Overall, the sample of candidate neutrino-emitter blazars displays properties compatible with those of the reference samples. We observe a mild tendency to prefer objects with intense radiation fields (which are typical of radiatively efficient accretors), and high radio power, such as high-excitation radio galaxies (HERGs). Among the blazars in our sample, 24 are detected in γ-rays; they cover various ranges of γ-ray luminosities, compatible with the overall population. Additionally, we show that the statistical tests commonly used in the literature need to be used with caution, as they are highly sensitive to the amount of censored data and the sample size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".