Multi-epoch Modeling of TXS 0506+056 and Implications for Long-term High-energy Neutrino Emission
Bibliographic record
Abstract
Abstract The IceCube report of a excess of 13 ± 5 neutrino events in the direction of the blazar TXS 0506+056 in 2014–2015 and the 2017 detection of a high-energy neutrino event, IceCube-170922A, during a gamma-ray flare from the same blazar, have revived the interest in scenarios for neutrino production in blazars. We perform comprehensive analyses on the long-term electromagnetic emission of TXS 0506+056 using optical, X-ray, and gamma-ray data from the All-Sky Automated Survey for Supernovae, the Neil Gehrels Swift Observatory, Monitor of All-sky X-ray Image, and the Fermi Large Area Telescope. We also perform numerical modeling of the spectral energy distributions (SEDs) in four epochs prior to 2017 with contemporaneous gamma-ray and lower-energy (optical and/or X-ray) data. We find that the multi-epoch SEDs are consistent with a hybrid leptonic scenario, where the gamma-rays are produced in the blazar zone via external inverse Compton scattering of accelerated electrons, and high-energy neutrinos are produced via the photomeson production process of co-accelerated protons. The multi-epoch SEDs can be satisfactorily explained with the same jet parameters and variable external photon density and electron luminosity. Using the maximal neutrino flux derived for each epoch, we put an upper limit of ∼0.4–2 on the muon neutrino number in 10 years of IceCube observations. Our results are consistent with the IceCube-170922A detection, which can be explained as an upper fluctuation from the average neutrino rate expected from the source, but in strong tension with the 2014–2015 neutrino flare.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".