Multi-messenger emission from the parsec-scale jet of the flat-spectrum radio quasar PKS 1502+106 coincident with high-energy neutrino IceCube-190730A
Bibliographic record
Abstract
Abstract On July 30th, 2019 IceCube detected a high-energy astrophysical muon neutrino candidate, IC-190730A with a 67% probability of astrophysical origin. The flat spectrum radio quasar (FSRQ) PKS 1502 +106 is in the error circle of the neutrino. Motivated by this observation, we study PKS 1502+106 as a possible source of IC-190730A. PKS 1502+106 was in a quiet state in terms of UV/optical/X-ray/γ-ray flux at the time of the neutrino alert, we therefore model the expected neutrino emission from the source during its average long-term state, and investigate whether the emission of IC-190730A as a result of the quiet long-term emission of PKS 1502+106 is plausible. We analyse UV/optical and X-ray data and collect additional observations from the literature to construct the multi-wavelength spectral energy distribution of PKS 1502+106. We perform leptohadronic modelling of the multi-wavelength emission of the source and determine the most plausible emission scenarios and the maximum expected accompanying neutrino flux. A model in which the multi-wavelength emission of PKS 1502+106 originates beyond the broad-line region and inside the dust torus is most consistent with the observations. In this scenario, PKS 1502+106 can have produced up to of order one muon neutrino with energy exceeding 100 TeV in the lifetime of IceCube. An appealing feature of this model is that the required proton luminosity is consistent with the average required proton luminosity if blazars power the observed ultra-high-energy-cosmic-ray flux and well below the source's Eddington luminosity. If such a model is ubiquitous among FSRQs, additional neutrinos can be expected from other bright sources with energy ≳ 10 PeV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".