Powerful extragalactic jets dissipate their kinetic energy far from the central black hole

The study addresses where powerful extragalactic (blazar) jets dissipate most of their kinetic energy into gamma-rays—a key question impacting models of particle acceleration, jet formation, collimation, and energy dissipation. The SEDs of such jets exhibit a two-peak structure with synchrotron and inverse Compton components; in high-power sources, external photon fields dominate the inverse Compton process (EC). The two leading candidate seed photon fields are the sub-parsec broad-line region (BLR) and the parsec-scale molecular torus (MT). Previous localization methods—using variability timescales, gamma-ray opacity/absorption, and correlations with VLBI-core events—have produced conflicting indications (inside the BLR, within the MT, or at/beyond the VLBI core). The paper proposes a robust, population-level diagnostic based on long-term average observables to determine the dominant gamma-ray emission location.

Prior work has argued for different gamma-ray emission sites: short gamma-ray variability suggests compact, sub-parsec regions within the BLR; TeV detections and lack of strong absorption features challenge the BLR scenario; coordinated optical polarization and VLBI radio behavior in some flares point to an emission site near the VLBI core, beyond the MT; individual flares in PKS 1510-089 show cases consistent with either the MT or the VLBI core. Much of this literature relies on single/few sources or rare states, leading to contradictory conclusions. Reverberation mapping and interferometry in radio-quiet AGN, combined with covering factor studies, indicate relatively constant energy densities for BLR and MT across sources, and prior estimates of their characteristic photon energies enable precise expectations for EC-driven observables.

The authors develop a diagnostic called the seed factor (SF), defined to be uniquely sensitive to the seed photon field at the gamma-ray emission site. SF is derived (details in Supplementary Information) in terms of observable SED parameters: SF = log10[(U0/ε0)^(1/2)] = −log10[3.22×10^4 × k1 × (ν13^2)/(ν22^13.3)] Gauss, where U0 is energy density of the seed photon field (cgs), ε0 is its characteristic photon energy (in units of electron rest mass), k1 is Compton dominance (IC peak luminosity/synchrotron peak luminosity) scaled by 10, ν13 is the synchrotron peak frequency in 10^13 Hz units, and ν22 is the IC peak frequency in 10^22 Hz units. The expected SF for a given seed field is computed from its U0 and ε0, which are well-constrained for BLR and MT. Because SF values differ significantly for BLR vs MT, the SF serves as a discriminator. Data: The team assembled 62 well-sampled, quasi-simultaneous multiwavelength SEDs of powerful FSRQs from four sources: Planck Collaboration subsamples, the Fermi-LAT Bright AGN Sample (LBAS), TANAMI targets, and multiple SEDs for two well-observed sources (3C 279, 3C 454.3). SEDs were required to have good frequency coverage, quasi-simultaneity, steady-state emission, and Thomson-regime IC scattering (ν_IC ≤ 10^24 Hz); SEDs failing these criteria were excluded. SED fitting: Peak frequencies and luminosities for synchrotron and IC components were obtained via maximum-likelihood regression with simulated annealing. Uncertainties used Wilks’ theorem in combination with bootstrapping, kernel density estimation, and polynomial interpolation. For each SED, SF and its uncertainty were determined from the four observables (ν_syn, L_syn, ν_IC, L_IC). Statistical analysis: The distribution of SF values was visualized via histograms and kernel density estimation (KDE) with Silverman’s rule bandwidth; uncertainties were incorporated through bootstrapping. Normality was assessed with a two-sided bootstrapped Kolmogorov–Smirnov (K–S) test. The SF distribution of powerful jets was compared with that of weak jets (non-EC-dominated) for consistency with EC. The observed median SF and its uncertainty were obtained via bootstrap (8×10^5 resamples). Finally, rejection significances were computed for hypotheses that the observed median equals the expected BLR or MT SF values by bootstrapping triplets of medians (observed, BLR proxy, MT proxy) and evaluating the number of standard deviations from zero in the difference distributions.

• Expected seed factors (from literature U0 and ε0): • SF_BLR = 3.29 ± 0.11 • SF_MT = 3.92 ± 0.11
• Observed seed factor distribution from 62 SEDs peaks within the 1σ interval of SF_MT; KDE nearly coincides with the MT value.
• Median observed seed factor: SF_median = 4.01 ± 0.10.
• Normality test of SF distribution: K–S rejection significance ≈ 1.42σ (p ≈ 0.16); cannot reject normality.
• Powerful-jet SF distribution differs from that of weak jets (non-EC), supporting an EC origin for powerful jets.
• Rejection significances for equality of medians: • |Obs_median − BLR_median| = 6.10σ (strongly rejects BLR as sole dominant site) • |Obs_median − MT_median| = 0.71σ (consistent with MT)
• Physical inference: The dominant gamma-ray emission and kinetic energy dissipation in powerful jets occurs at ~1 pc from the black hole (~10^4 Schwarzschild radii for a 10^9 M☉ BH), consistent with EC on molecular torus photons and inconsistent with a steady-state BLR origin or the VLBI core as the dominant site.

The results constrain jet physics by indicating negligible steady-state energy dissipation at radii <~1 pc. Within this region, the flow must collimate to a few-degree opening angle and accelerate to bulk Lorentz factors Γ ~ 10–50 (consistent with VLBI studies), while major particle acceleration and dissipation (~10% of jet power) occur beyond the BLR and within the parsec-scale molecular torus. The population-based approach, using quasi-simultaneous SEDs, mitigates biases from variability and interband integration mismatches that affect single-epoch or single-source studies. The peaked SF distribution near the MT value also argues against the VLBI core being the dominant gamma-ray site across the population. Improved characterization of the stratified, possibly wind-driven BLR and torus structures and any deviations from standard scaling relations would enable more precise localization and exploration of temporal/source-to-source variations in dissipation sites. Conversely, models of AGN environments and jet–environment coupling can be constrained by requiring consistency with the observed SF distribution.

The study introduces the seed factor, a robust diagnostic derived solely from observable SED peak properties, to localize the dominant gamma-ray emission region in powerful blazars. Applying this to 62 quasi-simultaneous SEDs shows that the SF distribution strongly favors the molecular torus over the broad-line region, indicating that powerful extragalactic jets typically dissipate their kinetic energy at ~1 pc from the central black hole via external Compton scattering on torus photons. This population-level result provides clear constraints on jet collimation, acceleration, and dissipation physics, and argues against the VLBI core as the dominant gamma-ray site. Future work with larger samples, multiple SEDs per source, and refined models of BLR/torus structure may permit finer localization, investigation of potential distributions of dissipation sites, and tests of temporal evolution within individual sources.

• The diagnostic is not applied to rapid variability states due to limited multi-band cadence; conclusions pertain to steady/quasi-steady emission captured in quasi-simultaneous SEDs.
• Single-SED localizations for individual sources are unreliable; robust localization for a given source would require many SEDs.
• Fitting assumes polynomial approximations to SED peaks and employs Wilks’ theorem; variability and model imperfections introduce additional, unmodeled uncertainties presumed to average out in the ensemble.
• SED selection requires good coverage and Thomson-regime IC; some states (e.g., Klein–Nishina-dominated) are excluded.
• Potential deviations or stratification in BLR/torus scaling relations could shift expected SF values slightly; more detailed environment models could refine the conclusions.