Probing a Cosmogenic Origin of Astrophysical Neutrinos and Cosmic Rays Using Gamma-Ray Observations of TXS 0506+056
Bibliographic record
Abstract
Abstract In 2017 September, a high-energy neutrino event detected by the IceCube Neutrino Observatory (IceCube-170922A) was associated, at the 3 σ level, with a gamma-ray flare from the blazar TXS 0506+056. Cosmic rays that are accelerated in astrophysical sources can escape from their jets and interact with background radiation fields. Interactions with the extragalactic background light can produce pions and hence neutrinos, while interactions with the cosmic microwave background predominantly drive inverse Compton scattering, contributing to electromagnetic cascades in intergalactic space. The resulting secondary gamma-ray emission can be detected with high-energy gamma-ray telescopes. Here, we report on a new search for such cosmogenic cascade emission from the blazar TXS 0506+056, using a combined data set from the Fermi Large Area Telescope and VERITAS. We compare the gamma-ray spectrum and neutrino observations with the predictions of cosmic-ray-induced cascades in intergalactic space. The observed gamma-ray spectrum is modeled as a combination of the primary spectrum and the cascade spectrum. We apply a Monte Carlo simulation with a Δ χ 2 -based likelihood analysis to jointly determine the best-fit parameters of a proton emission spectrum describing the data and derive constraints on the proton escape luminosity. Assuming an LP primary photon spectrum, we find consistency with a proton injection spectral index of α p ≃ 2.0 and a cutoff energy of E p,max ≃ 1.3 × 10 16 eV and constrain the isotropic proton escape luminosity to 1 × 10 44 erg s −1 ≲ L p,esc ≲ 3 × 10 45 erg s −1 at the 90% confidence level.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".