Pith. sign in

REVIEW 3 major objections 7 minor 41 references

In the jet of 3C 120, gamma-ray flares lead radio outbursts by months and feed a dual engine of slow precession plus fast internal shocks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-11 15:31 UTC pith:S26IYPA6

load-bearing objection Solid 13-year multi-wavelength case study of 3C 120 with usable lag hierarchy and clear knot-ejection sequences; dual-mechanism framing is plausible but rests on lags that lack a quantitative red-noise/precession control. the 3 major comments →

arxiv 2607.04666 v1 pith:S26IYPA6 submitted 2026-07-06 astro-ph.GA astro-ph.HE

Observational Study of Multi-wavelength Synergistic Effects in 3C 120

Yuting He , Ming Zhang , Qi Yuan , Nenghui Liao This is my paper
classification astro-ph.GA astro-ph.HE
keywords active galactic nucleiradio galaxiesjetsgamma-ray astronomyvery long baseline interferometry3C 120jet precessioninternal shocks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tracks thirteen years of gamma-ray, optical, and high-resolution radio data on the nearby radio galaxy 3C 120 to pin down where and how energy is released inside its relativistic jet. Cross-correlations show that gamma-ray flares systematically precede radio core brightening by roughly eight months at 43 GHz and eleven months at 15 GHz, placing the high-energy dissipation zone upstream of the radio cores and matching the expected frequency-dependent opacity shift. During the major flares the radio core brightens, polarized flux surges, the electric-vector angle swings, and superluminal knots are later ejected. Internal radio correlations then separate two drivers: long-term changes in jet orientation and baseline flux are geometrically set by a multi-year precession of the jet, while the rapid, highly polarized bursts are produced by short-lived internal shocks racing down the jet channel. A sympathetic reader cares because the same dual picture offers a concrete way to link high-energy particle acceleration to the observable structure of jets across the broader AGN population.

Core claim

Gamma-ray flares in 3C 120 lead the radio core emission by 11.08 months at 15 GHz and 8.27 months at 43 GHz, locating the high-energy dissipation zone upstream of the radio cores; the subsequent radio outbursts are accompanied by core brightening, polarized-flux surges, EVPA rotations and superluminal knot ejections, which together support a dual mechanism in which secular jet precession sets the long-term kinematic and flux baseline while short-lived internal shocks drive the rapid polarimetric bursts.

What carries the argument

The dual-mechanism framework: secular jet precession that geometrically modulates long-term flux and jet position angle, overlaid by short-lived internal shocks that produce the rapid total-intensity and polarimetric flares.

Load-bearing premise

The measured positive time lags are taken as pure light-travel and opacity delays between a fixed upstream gamma-ray zone and the radio cores, rather than being dominated by red-noise aliasing or geometric Doppler changes from the same precession.

What would settle it

A future multi-wavelength campaign that finds either zero or negative gamma-ray-to-radio lags at both 15 and 43 GHz during a major flare, or that shows the rapid polarization surges occurring without any accompanying superluminal knot ejection, would directly contradict the claimed sequence and dual mechanism.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • High-energy dissipation in similar jets occurs on parsec scales upstream of the radio core and can be located by frequency-dependent time lags.
  • Radio polarimetric bursts become reliable markers of internal shocks once the slow precession baseline is subtracted.
  • The same dual-driver picture should apply to the broader blazar population, separating geometric modulation from particle-acceleration events.
  • Future multi-messenger timing (neutrinos, TeV photons) can be anchored to the same upstream zone identified by the radio lags.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If precession and internal shocks are truly separable, the amplitude of rapid polarimetric flares should be independent of the instantaneous precession phase once the Doppler baseline is removed.
  • The same lag hierarchy, if measured in a statistical sample of FR I jets, would test whether the upstream dissipation zone is a universal feature rather than a peculiarity of 3C 120.
  • Simultaneous mm-VLBI and gamma-ray monitoring could resolve whether the first radio response occurs at still higher frequencies and even shorter lags, tightening the spatial map of the dissipation region.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This paper presents a 13-year (2012–2025) multi-wavelength analysis of the broad-line radio galaxy 3C 120, combining Fermi-LAT γ-ray light curves, ASAS-SN optical photometry, and VLBA monitoring at 15 GHz (MOJAVE) and 43 GHz (BEAM-ME). Using ZDCF, the authors report that γ-ray variations lead radio core emission by 11.08^{+4.03}_{-1.88} months at 15 GHz and 8.27^{+3.45}_{-5.55} months at 43 GHz, which they interpret as placing the high-energy dissipation zone upstream of the radio cores and supporting an opacity-driven core-shift picture. Event-by-event comparison of major γ-ray flaring epochs with radio light curves and 43 GHz imaging shows core brightening, polarized-flux surges, EVPA rotations, and subsequent superluminal knot ejections. Internal ICCF correlations among core flux, jet PA, polarized flux, and EVPA are synthesized into a dual-mechanism framework: secular jet precession (~12.3 yr, from prior work) modulates long-term kinematics and flux baselines, while short-lived internal shocks drive rapid polarimetric bursts.

Significance. If the lag hierarchy and dual-mechanism interpretation hold, the work provides a concrete, multi-epoch observational link between high-energy dissipation and parsec-scale jet response in a nearby, moderately inclined FR I source—valuable because extreme Doppler beaming is less severe than in blazars. Strengths include a carefully documented VLBI reduction path (SAND, image-plane Gaussian fitting, ODR jet PA), standard FR/RSS Monte-Carlo lag uncertainties, and direct morphological sequences (Figures 10–12, Table 3) that independently support shock-driven knot ejections. The dual-mechanism framing is a useful organizing synthesis for the community. The result is incremental rather than transformative, but it is a solid contribution to AGN jet physics if the statistical control of the lags is tightened.

major comments (3)
  1. [Section 4.1, Figure 5] Section 4.1 and Figure 5: The central claim that the positive ZDCF peaks (τ_15 = 11.08^{+4.03}_{-1.88} mo, τ_43 = 8.27^{+3.45}_{-5.55} mo) represent physical light-travel/opacity delays rests on FR/RSS for lag uncertainty, not on a false-alarm test against red-noise surrogates. The paper correctly cites Max-Moerbeck et al. (2014) when dismissing secondary negative peaks as aliasing, but does not show PSD-matched (power-law or CARMA) Monte-Carlo light curves demonstrating that the reported positive peaks exceed the red-noise false-alarm rate at the claimed significance. Because the same precession later invoked for the radio baseline can imprint quasi-periodic structure on both bands, a quantitative red-noise (and, ideally, precession-modulated) control is load-bearing for interpreting the lags as a fixed upstream γ-ray zone.
  2. [Abstract; Section 4.1; Section 6] Abstract, Section 4.1, and Section 6: The manuscript emphasizes a “frequency-dependent temporal hierarchy” that “corroborat[es] the opacity-driven core-shift effect,” yet the text itself states that the two lags “overlap and are statistically consistent with each other” once 1σ uncertainties are included. With the present error bars the 15 GHz vs 43 GHz ordering is only marginally preferred. Either the frequency hierarchy should be stated more cautiously (consistent with, but not uniquely requiring, core-shift), or additional analysis (e.g., joint lag constraints, core-shift literature comparison for 3C 120) should be supplied to justify the stronger wording.
  3. [Section 5.2, Figure 6] Section 5.2 and Figure 6: The global optical–radio ZDCF is essentially flat (r_max ≈ 0.14–0.27), which the authors attribute to a mismatch between shock-dominated optical residuals and precession-dominated radio envelopes. They then argue for “localized multi-band synchronicity” by visual inspection of LOWESS-detrended residuals and monthly-binned γ-ray data. Without a quantitative local (flare-window) cross-correlation or a precession-subtracted residual CCF, this step remains qualitative and weakens the multi-wavelength coupling claim relative to the stronger γ-ray–radio and morphological evidence.
minor comments (7)
  1. [Table 1] Table 1: Reported core-flux uncertainties (e.g., 0.8309 ± 0.0003 Jy) appear unrealistically small for VLBI image-plane fits; clarify whether these are formal fit errors only and, if so, note typical absolute calibration systematics (~5–10%).
  2. [Section 2.2] Section 2.2: The additive v/g zero-point alignment and LOWESS window fraction of 0.05 are reasonable but free parameters; a brief sensitivity check (or statement that lag results are unchanged under modest window changes) would strengthen reproducibility.
  3. [Section 3.1] Section 3.1: ZDCF N_min = 11 is stated without justification or sensitivity test; a short note on how peak lag and r_max change for N_min = 8–15 would help.
  4. [Figures 7–8] Figures 7–8: The multi-panel light curves are information-dense; ensuring that the shaded flaring windows and inset EVPA/polarized-flux scales remain legible in print (and that color is not the sole discriminator) would improve accessibility.
  5. [Section 4.3, Table 2] Section 4.3 / Table 2: The large opposite-sign lags for core flux vs jet PA at 15 GHz (+39.6 mo) versus 43 GHz (−12.5 mo) are interesting but only qualitatively linked to stratified opacity; a short quantitative consistency check with the adopted precession period would help the dual-mechanism narrative.
  6. [Section 2.1] EVPA unwrapping (Section 2.1): The ±π correction when |Δχ| > 120° is standard; state whether any epochs required manual intervention or were discarded due to ambiguity.
  7. [Data / Acknowledgments] References / data: Explicitly list the public MOJAVE and BEAM-ME epoch ranges used and the DOI or repository for any machine-readable light curves/tables to aid reuse.

Circularity Check

0 steps flagged

No significant circularity: ZDCF lags, morphological associations, and dual-mechanism synthesis are independent observational results, not forced by self-definition, fitted inputs, or load-bearing self-citation chains.

full rationale

The paper's central quantitative claims (γ-ray leading radio by 11.08^{+4.03}_{-1.88} mo at 15 GHz and 8.27^{+3.45}_{-5.55} mo at 43 GHz via ZDCF with FR/RSS Monte Carlo; core brightening + polarized-flux surges + EVPA rotations + superluminal knot ejections tracked on 43 GHz VLBA maps) are computed directly from the multi-wavelength time series and image-plane model fits. The ~12.3 yr precession period is imported only as qualitative context from the external Caproni & Abraham (2004) optical/helical-jet study and is never fitted to the present light curves or used to generate the reported lags. Chen et al. (2025) supplies the adaptive-binned Fermi-LAT light curve and Bayesian Blocks flare windows (with one overlapping co-author), and the SAND pipeline (Zhang) supplies the VLBI reduction; both are ordinary data/method citations, not uniqueness theorems or ansatzes that force the lag values or dual-mechanism interpretation. No equation equates a claimed prediction to a fitted input by construction, no self-definitional loop appears, and the dual-mechanism framework is an after-the-fact synthesis of independent observables rather than a derivation that reduces to its premises. Secondary negative CCF peaks are dismissed by external red-noise literature (Max-Moerbeck et al. 2014) plus the standard opacity paradigm; that dismissal is interpretive, not circular. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 1 invented entities

The central lag and dual-mechanism claims rest on standard AGN-jet opacity and shock-in-jet assumptions plus a handful of analysis choices (binning, detrending window, flare selection thresholds) and one external precession period. No new particles or forces are invented; the dual-mechanism framework is an interpretive synthesis rather than a postulated entity.

free parameters (5)
  • ZDCF N_min = 11
    Minimum of 11 independent pairs per lag bin; controls the trade-off between temporal resolution and statistical significance of the reported lag peaks.
  • LOWESS window fraction = 0.05
    Temporal window of 0.05 used to subtract the long-term optical baseline before ZDCF; directly affects the residual light curve that is cross-correlated with radio.
  • FR/RSS Monte-Carlo realizations = 1000 / 10000
    1000 (ZDCF) and 10 000 (ICCF) flux-randomization / random-subset simulations set the reported 1σ lag uncertainties.
  • EVPA unwrap threshold = 2π/3
    ±π correction applied whenever consecutive EVPA jumps exceed 120°; controls continuity of the polarization angle time series used in ICCF.
  • Bayesian Blocks flare thresholds
    Peak-flux and duration cuts that define the three major γ-ray epochs (and relegate others to ‘low-state’); selection is taken from Chen et al. (2025) without re-derivation.
axioms (5)
  • domain assumption Synchrotron self-absorption opacity decreases with frequency, so the τ=1 surface (radio core) lies farther downstream at lower frequencies (core-shift effect).
    Invoked throughout Sections 1, 4.1 and 5 to convert the observed frequency-dependent lags into a spatial hierarchy.
  • domain assumption Positive CCF lag of γ-ray relative to radio implies the γ-ray zone is spatially upstream of the radio core.
    Standard light-travel / propagation interpretation used to claim the dissipation-zone location (Section 4.1).
  • domain assumption 3C 120 jet precesses with a period of ~12.3 yr.
    Taken from Caproni & Abraham (2004) and used to attribute long-term jetPA and baseline-flux variations to geometry (Sections 1, 4.3, 5.1).
  • ad hoc to paper Negative-lag secondary peaks in the ZDCF are red-noise artifacts rather than physical radio-leading-γ-ray signals.
    Stated in Section 4.1 without quantitative red-noise simulations; required to discard the secondary peaks and retain only the positive lags.
  • domain assumption Image-plane Gaussian component fitting (SAND) faithfully recovers core flux, jetPA and polarization even for non-Gaussian downstream knots.
    Methodological premise of Section 2.1 that underpins all radio light curves and morphological sequences.
invented entities (1)
  • dual-mechanism framework (secular precession + short-lived internal shocks) no independent evidence
    purpose: Unifies long-term geometric trends with rapid polarimetric bursts under a single interpretive picture.
    Presented as the paper’s ultimate synthesis (Abstract, Sections 4.3, 5, 6). It is an organizing narrative rather than a new physical entity; independent evidence is the same multi-wavelength data set used to construct it.

pith-pipeline@v1.1.0-grok45 · 24244 in / 3795 out tokens · 32200 ms · 2026-07-11T15:31:12.053287+00:00 · methodology

0 comments
read the original abstract

The energy dissipation and particle acceleration mechanisms within relativistic jets remain fundamental questions in active galactic nuclei (AGN) research. In this paper, we present a comprehensive 13-year (2012-2025) multi-wavelength study of the broad-line radio galaxy 3C 120, utilizing Fermi-LAT ($\gamma$-ray), ASAS-SN (optical), and high-resolution VLBA (15 GHz and 43 GHz) monitoring. Cross-correlation analyses reveal that $\gamma$-ray flares lead radio emission by $11.08_{-1.88}^{+4.03}$ months at 15 GHz and $8.27_{-5.55}^{+3.45}$ months at 43 GHz. This frequency-dependent temporal hierarchy positions the high-energy dissipation zone upstream of the radio core, corroborating the opacity-driven core-shift effect. By tracking the parsec-scale jet morphology during major $\gamma$-ray flaring epochs, we demonstrate that radio outbursts systematically coincide with compact core brightening, pronounced surges in polarized flux, abrupt electric vector position angle rotations, and the subsequent ejection of superluminal knots. Ultimately, our internal radio correlations suggest that jet dynamics are governed by a dual mechanism: long-term kinematic and flux baseline variations are geometrically modulated by a secular jet precession, while rapid, highly energetic polarimetric bursts are driven by short-lived internal shocks propagating down the jet channel.

Figures

Figures reproduced from arXiv: 2607.04666 by Ming Zhang, Nenghui Liao, Qi Yuan, Yuting He.

Figure 1
Figure 1. Figure 1: Schematic of the SAND VLBI data-reduction pipeline, illustrating both uv-plane and image-plane fitting pathways [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Multi-wavelength light curves of 3C 120. The four vertically arranged panels display radiation variations across different bands: (top panel) γ-ray flux detected by Fermi-LAT; (second panel) aligned optical fluxes in the v and g bands from the ASAS-SN survey, superimposed with the long-term LOWESS baseline; (third panel) 43 GHz radio flux density from the VLBA-BU-BLAZAR monitoring program; (fourth panel) 1… view at source ↗
Figure 3
Figure 3. Figure 3: Schematic of the ZDCF methodology and global error estimation. (a) Data pairing and discrete time lag (∆τij ) extraction from unevenly sampled light curves. (b) Equal-population binning (Nmin = 11) of discrete pairs to construct the ZDCF profile. (c) Centroid distribution from monte carlo FR/RSS simulations, defining the final physical time lag (median) and its 1σ uncertainties. Detailed definitions of the… view at source ↗
Figure 4
Figure 4. Figure 4: Schematic of the ICCF methodology and error estimation. (a) Linear interpolation and flux randomization of observed data. (b) Simulated CCF ensemble defining the 3σ confidence envelopes. (c) Peak centroid (µ) and uncertainties (στ , σcoeff ) extracted via gaussian fitting and monte carlo statistics. Detailed definitions are provided in Section 3.2. 4. RESULTS 4.1. Multi-wavelength Correlations and Flare An… view at source ↗
Figure 5
Figure 5. Figure 5: ZDCF profiles for the cross-correlation between Fermi-LAT γ-ray flux and radio core flux densities of 3C 120. 16 12 8 4 0 4 8 12 16 Time Lag (months) 1.0 0.8 0.6 0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0 Correlation Coefficient time lag = 6.75 +3.61 5.44 months rmax =0.268 +0.119 0.123 optical vs 15 GHz 16 12 8 4 0 4 8 12 16 Time Lag (months) time lag = 3.63 +4.61 3.42 months rmax =0.140 +0.100 0.101 optical vs 43 G… view at source ↗
Figure 6
Figure 6. Figure 6: ZDCF profiles for the cross-correlation between the LOWESS-detrended optical residuals and radio core flux densities of 3C 120 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Multi-parameter light curves and corresponding parsec-scale morphological evolution of 3C 120 at 15 GHz from the MOJAVE monitoring program. Top panels: From top to bottom, the light curves display core flux density, jet position angle, polarized flux, and electric vector position angle. The right panels provide a magnified view of Flare II. Bottom panels: Sequence of 15 GHz VLBA total intensity contour map… view at source ↗
Figure 8
Figure 8. Figure 8: Multi-parameter light curves and corresponding parsec-scale morphological evolution of 3C 120 at 43 GHz from the VLBA-BU-BLAZAR monitoring program. Top panels: From top to bottom, the light curves display core flux density, jet position angle, polarized flux, and electric vector position angle. The right panels provide a magnified view of Flare II. Bottom panels: Sequence of 43 GHz VLBA total intensity con… view at source ↗
Figure 9
Figure 9. Figure 9: Interpolation Cross-Correlation Function analyses of radio parameter pairs for the broad-line radio galaxy 3C 120, covering 15 GHz (left column) and 43 GHz (right column) bands. Panels from top to bottom for each frequency band: core flux vs jetPA, jetPA vs EVPA, core flux vs EVPA, pollflux vs EVPA, and core flux vs pollflux. Green solid curve: observed ICCF; gray curves: rigorous 3σ confidence envelopes f… view at source ↗
Figure 10
Figure 10. Figure 10 [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Sequence of 43 GHz VLBA total intensity contour maps of 3C 120 during Flare III. The sequence captures the core region rapidly transitioning from a weak, diffuse structure to a highly compact, bright emission region peaking at 2.01 Jy at epoch 1UQ. Contours and label descriptions are the same as those in Figures 10. (as highlighted in Figures 7 and 8). The characteristic timescales of these polarimetric b… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

41 extracted references · 5 canonical work pages

  1. [1]

    1997, in Astrophysics and Space Science

    Alexander, T. 1997, in Astrophysics and Space Science

    1997

  2. [2]

    218, Astronomical Time Series, ed

    Library, Vol. 218, Astronomical Time Series, ed. D. Maoz, A. Sternberg, & E. M. Leibowitz, 163, doi: 10.1007/978-94-015-8941-3 14

    doi:10.1007/978-94-015-8941-3

  3. [3]

    D., & Znajek, R

    Blandford, R. D., & Znajek, R. L. 1977, MNRAS, 179, 433, doi: 10.1093/mnras/179.3.433

    doi:10.1093/mnras/179.3.433 1977

  4. [4]

    2004, MNRAS, 349, 1218, doi: 10.1111/j.1365-2966.2004.07550.x

    Caproni, A., & Abraham, Z. 2004, MNRAS, 349, 1218, doi: 10.1111/j.1365-2966.2004.07550.x

    doi:10.1111/j.1365-2966.2004.07550.x 2004

  5. [5]

    L., Grandi, P., et al

    Casadio, C., G´ omez, J. L., Grandi, P., et al. 2015, ApJ, 808, 162, doi: 10.1088/0004-637X/808/2/162

    doi:10.1088/0004-637x/808/2/162 2015

  6. [6]

    P., Jorstad, S

    Chatterjee, R., Marscher, A. P., Jorstad, S. G., et al. 2009, ApJ, 704, 1689, doi: 10.1088/0004-637X/704/2/1689

    doi:10.1088/0004-637x/704/2/1689 2009

  7. [7]

    2025, A&A, 702, A129, doi: 10.1051/0004-6361/202453146

    Chen, R.-Q., Liao, N.-H., Jiang, X., & Fan, Y.-Z. 2025, A&A, 702, A129, doi: 10.1051/0004-6361/202453146

    work page doi:10.1051/0004-6361/202453146 2025

  8. [8]

    Cleveland, W. S. 1979, Journal of the American Statistical Association, 74, 829, doi: 10.1080/01621459.1979.10481038

    doi:10.1080/01621459.1979.10481038 1979

  9. [9]

    H., Meier, D

    Cohen, M. H., Meier, D. L., Arshakian, T. G., et al. 2015, ApJ, 803, 3, doi: 10.1088/0004-637X/803/1/3

    doi:10.1088/0004-637x/803/1/3 2015

  10. [10]

    A., & Krolik, J

    Edelson, R. A., & Krolik, J. H. 1988, ApJ, 333, 646, doi: 10.1086/166773

    doi:10.1086/166773 1988

  11. [11]

    2014, MNRAS, 441, 1899, doi: 10.1093/mnras/stu540

    Fuhrmann, L., Larsson, S., Chiang, J., et al. 2014, MNRAS, 441, 1899, doi: 10.1093/mnras/stu540

    doi:10.1093/mnras/stu540 2014

  12. [12]

    M., & Sparke, L

    Gaskell, C. M., & Sparke, L. S. 1986, ApJ, 305, 175, doi: 10.1086/164238

    doi:10.1086/164238 1986

  13. [13]

    2010, MNRAS, 402, 497, doi: 10.1111/j.1365-2966.2009.15898.x

    Ghisellini, G., Tavecchio, F., Foschini, L., et al. 2010, MNRAS, 402, 497, doi: 10.1111/j.1365-2966.2009.15898.x

    doi:10.1111/j.1365-2966.2009.15898.x 2010

  14. [14]

    Greisen, E. W. 2003, in Astrophysics and Space Science

    2003

  15. [15]

    285, Information Handling in Astronomy - Historical Vistas, ed

    Library, Vol. 285, Information Handling in Astronomy - Historical Vistas, ed. A. Heck, 109, doi: 10.1007/0-306-48080-8 7

    doi:10.1007/0-306-48080-8

  16. [16]

    J., Hey, D., et al

    Hart, K., Shappee, B. J., Hey, D., et al. 2023, arXiv e-prints, arXiv:2304.03791, doi: 10.48550/arXiv.2304.03791

    Pith/arXiv arXiv doi:10.48550/arxiv.2304.03791 2023

  17. [17]

    G., Marscher, A

    Jorstad, S. G., Marscher, A. P., Morozova, D. A., et al. 2017, ApJ, 846, 98, doi: 10.3847/1538-4357/aa8407

    doi:10.3847/1538-4357/aa8407 2017

  18. [18]

    S., Shappee, B

    Kochanek, C. S., Shappee, B. J., Stanek, K. Z., et al. 2017, PASP, 129, 104502, doi: 10.1088/1538-3873/aa80d9

    doi:10.1088/1538-3873/aa80d9 2017

  19. [19]

    G., Pushkarev, A

    Kramarenko, I. G., Pushkarev, A. B., Kovalev, Y. Y., et al. 2022, MNRAS, 510, 469, doi: 10.1093/mnras/stab3358

    doi:10.1093/mnras/stab3358 2022

  20. [20]

    M., Jorstad, S

    Larionov, V. M., Jorstad, S. G., Marscher, A. P., & Smith, P. S. 2016, Galaxies, 4, 43, doi: 10.3390/galaxies4040043 Le´ on-Tavares, J., Lobanov, A. P., Chavushyan, V. H., et al. 2010, ApJ, 715, 355, doi: 10.1088/0004-637X/715/1/355

    work page doi:10.3390/galaxies4040043 2016

  21. [21]

    2018, ApJ, 866, 137, doi: 10.3847/1538-4357/aae2b7

    Liodakis, I., Hovatta, T., Huppenkothen, D., et al. 2018, ApJ, 866, 137, doi: 10.3847/1538-4357/aae2b7

    doi:10.3847/1538-4357/aae2b7 2018

  22. [22]

    L., Aller, M

    Lister, M. L., Aller, M. F., Aller, H. D., et al. 2018, ApJS, 234, 12, doi: 10.3847/1538-4365/aa9c44

    doi:10.3847/1538-4365/aa9c44 2018

  23. [23]

    I., & Gabuzda, D

    Lyutikov, M., Pariev, V. I., & Gabuzda, D. C. 2005, MNRAS, 360, 869, doi: 10.1111/j.1365-2966.2005.08954.x

    doi:10.1111/j.1365-2966.2005.08954.x 2005

  24. [24]

    P., & Gear, W

    Marscher, A. P., & Gear, W. K. 1985, ApJ, 298, 114, doi: 10.1086/163592

    doi:10.1086/163592 1985

  25. [25]

    P., Jorstad, S

    Marscher, A. P., Jorstad, S. G., G´ omez, J.-L., et al. 2002, Nature, 417, 625, doi: 10.1038/nature00772

    doi:10.1038/nature00772 2002

  26. [26]

    P., Jorstad, S

    Marscher, A. P., Jorstad, S. G., D’Arcangelo, F. D., et al. 2008, Nature, 452, 966, doi: 10.1038/nature06895 20

    doi:10.1038/nature06895 2008

  27. [27]

    L., et al

    Max-Moerbeck, W., Hovatta, T., Richards, J. L., et al. 2014, MNRAS, 445, 428, doi: 10.1093/mnras/stu1749

    doi:10.1093/mnras/stu1749 2014

  28. [28]

    J., Bagri, D

    Napier, P. J., Bagri, D. S., Clark, B. G., et al. 1994, IEEE Proceedings, 82, 658, doi: 10.1109/5.284733

    doi:10.1109/5.284733 1994

  29. [29]

    B., Gabuzda, D

    Pushkarev, A. B., Gabuzda, D. C., Vetukhnovskaya, Y. N., & Yakimov, V. E. 2005, MNRAS, 356, 859, doi: 10.1111/j.1365-2966.2004.08535.x

    doi:10.1111/j.1365-2966.2004.08535.x 2005

  30. [30]

    B., Kovalev, Y

    Pushkarev, A. B., Kovalev, Y. Y., & Lister, M. L. 2010, ApJL, 722, L7, doi: 10.1088/2041-8205/722/1/L7

    work page doi:10.1088/2041-8205/722/1/l7 2010

  31. [31]

    M., Villata, M., Acosta-Pulido, J

    Raiteri, C. M., Villata, M., Acosta-Pulido, J. A., et al. 2017, Nature, 552, 374, doi: 10.1038/nature24623

    doi:10.1038/nature24623 2017

  32. [32]

    2020, A&A, 642, A59, doi: 10.1051/0004-6361/202038324

    Rakshit, S. 2020, A&A, 642, A59, doi: 10.1051/0004-6361/202038324

    doi:10.1051/0004-6361/202038324 2020

  33. [33]

    J., Prieto, J

    Shappee, B. J., Prieto, J. L., Grupe, D., et al. 2014, ApJ, 788, 48, doi: 10.1088/0004-637X/788/1/48

    doi:10.1088/0004-637x/788/1/48 2014

  34. [34]

    2021, Galaxies, 9, 37, doi: 10.3390/galaxies9020037

    Tavecchio, F. 2021, Galaxies, 9, 37, doi: 10.3390/galaxies9020037

    work page doi:10.3390/galaxies9020037 2021

  35. [35]

    Villata, M., & Raiteri, C. M. 1999, A&A, 347, 30

    1999

  36. [36]

    R., Jorstad, S

    Weaver, Z. R., Jorstad, S. G., Marscher, A. P., et al. 2022, ApJS, 260, 12, doi: 10.3847/1538-4365/ac589c

    doi:10.3847/1538-4365/ac589c 2022

  37. [37]

    J., & Peterson, B

    White, R. J., & Peterson, B. M. 1994, PASP, 106, 879, doi: 10.1086/133456

    doi:10.1086/133456 1994

  38. [38]

    F., & Kokhirova, G

    Yuan, Q., Zhang, M., Liu, X., Jiang, P. F., & Kokhirova, G. I. 2023, ApJ, 949, 20, doi: 10.3847/1538-4357/acc5ec

    doi:10.3847/1538-4357/acc5ec 2023

  39. [39]

    Zensus, J. A. 1997, ARA&A, 35, 607, doi: 10.1146/annurev.astro.35.1.607

    doi:10.1146/annurev.astro.35.1.607 1997

  40. [40]

    2016, SAND: Automated VLBI imaging and analyzing pipeline,, Astrophysics Source Code Library, record ascl:1605.015 http://ascl.net/1605.015

    Zhang, M. 2016, SAND: Automated VLBI imaging and analyzing pipeline,, Astrophysics Source Code Library, record ascl:1605.015 http://ascl.net/1605.015

    2016

  41. [41]

    2018, MNRAS, 473, 4505, doi: 10.1093/mnras/stx1513

    Zhang, M., Collioud, A., & Charlot, P. 2018, MNRAS, 473, 4505, doi: 10.1093/mnras/stx1513

    work page doi:10.1093/mnras/stx1513 2018