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arxiv: 2604.05028 · v1 · submitted 2026-04-06 · 🌌 astro-ph.HE

Recognition: 2 theorem links

· Lean Theorem

Characterizing the Gamma-ray Emission from Low-Luminosity AGN

Chris Karwin , Nikita Khatiya , Margot Boughelilba , Xiurui Zhao , Anita Reimer , Marco Ajello
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:37 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords low-luminosity AGNgamma-ray emissionFermi LATstacking analysissynchrotron self-Comptonjetsstar formationactive galactic nuclei
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The pith

Fermi LAT data show gamma-ray emission from low-luminosity AGN, with new individual detection and stacked subthreshold signal.

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

The paper examines all low-luminosity AGN from the Palomar spectroscopic survey using 14.4 years of Fermi Large Area Telescope observations. It reports a new individual detection of one LLAGN and applies a stacking technique to reveal a collective signal from the subthreshold population. The stacked signal aligns with star-formation activity in the host galaxies, though a contribution from compact jets remains possible, while the individually detected sources point to jet-dominated emission. Spectral modeling for a subset of sources indicates that the gamma rays arise from synchrotron self-Compton processes in an inner jet region that is weakly magnetized, particle-dominated, and slowly moving. This work characterizes the high-energy output of the most common AGN in the local universe and releases a public Python library for similar stacking analyses.

Core claim

Our analysis results in a new detection of one LLAGN, as well as a detection of the subthreshold population using a stacking technique. We find that the signal from the subthreshold sample is consistent with being dominated by star-formation activity, although a contribution from compact jets or a mixed contribution from jetted and non-jetted systems is also feasible. On the other hand, the individually detected LLAGN are likely dominated by jet emission. We perform detailed spectral modeling for a subset of these sources and find that the gamma-ray signal can be explained by synchrotron self-Compton radiation, if the inner jet emission region is weakly magnetized with its total energy being

What carries the argument

Stacking technique for subthreshold Fermi-LAT sources combined with synchrotron self-Compton modeling of inner jet emission under conditions of weak magnetization and particle dominance.

If this is right

  • Individually detected LLAGN produce gamma rays primarily through jet processes rather than accretion disks.
  • The subthreshold LLAGN population contributes gamma rays consistent with star-formation activity in their host galaxies.
  • Inner jets in LLAGN can be described as weakly magnetized, particle-dominated, and slowly moving to match the observed spectra.
  • The released Python stacking library enables statistical studies of other faint source populations with LAT data.

Where Pith is reading between the lines

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

  • LLAGN may contribute only modestly to the overall extragalactic gamma-ray background compared to brighter AGN.
  • The stacking approach could be extended to search for hidden signals in other classes of faint high-energy sources.
  • If jet emission proves common but often subthreshold, targeted deeper observations might resolve more individual LLAGN.

Load-bearing premise

The detected and stacked gamma-ray signals originate from the LLAGN targets rather than residual background, cosmic-ray interactions, or unrelated sources.

What would settle it

If higher-resolution multiwavelength follow-up shows that the gamma-ray positions or spectra do not match the modeled LLAGN locations and jet conditions.

Figures

Figures reproduced from arXiv: 2604.05028 by Anita Reimer, Chris Karwin, Marco Ajello, Margot Boughelilba, Nikita Khatiya, Xiurui Zhao.

Figure 1
Figure 1. Figure 1: Stacked TS profile for the sample of sub￾threshold LLAGN. The color scale indicates the TS, and the plus sign indicates the location of the max￾imum value, with a TS = 31.2 (5.2 σ). Significance contours (for 2 degrees of freedom) are overlaid on the plot showing the 68%, 90%, and 99% confidence levels, corresponding to ∆TS = 2.30, 4.61, and 9.21, respectively. 0 25 50 75 100 125 150 175 Number of Stacked … view at source ↗
Figure 2
Figure 2. Figure 2: Maximum TS as a function of number of stacked sources, where the sources are ranked in order of increasing TS. We can therefore conclude that the signal is not being dominated by just a few sources. We obtain the spectrum of the subthreshold sources by sampling spectral parameters within the 1 σ confidence region of the stacked profile. The corresponding butterfly plot is shown in the upper left panel of … view at source ↗
Figure 3
Figure 3. Figure 3: SEDs (black data points) and butterfly plots (purple bands) for the subthreshold sources and the significant sources, as specified in the legends. The error for the flux data points and the bands is at the 1 σ confidence level. Upper limits for the SEDs are plotted for bins with TS<9, and they are shown at the 95% confidence level. The lower x-axis gives the energy, and the upper x-axis gives the frequency… view at source ↗
Figure 4
Figure 4. Figure 4: , we show separate TS profiles for the Seyferts, LINERs, and transition nuclei. The corresponding best-fit spectral parameters are reported in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Stacked profiles for different subsets of the full sample: spirals and non-spirals. The color scale and contours are the same as described in [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: TS versus Photon Index (Γ), showing the subthreshold sources with their spiral and non￾spiral classifications. The sources showing a jet￾ted morphology in the radio band from Baldi et al. (2021) are denoted by yellow open circles. photon index versus TS for the subthreshold sample (for sources with TS≥4). The sources with jetted morphology have been identified from radio observations, as described in Baldi… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of different source properties for the control sample and subthreshold LLAGN sample. Upper left: Hubble Type index; Upper right: distance; Middle left: BH mass, normalized to the mass of the Sun; Middle right: Absolute blue magnitude of the entire galaxy (assuming the given distance given in the upper right), corrected for Galactic and internal extinction; Bottom left: Absolute blue magnitude o… view at source ↗
Figure 11
Figure 11. Figure 11: Stacked profile for the control sample. The color scale shows the TS, and the plus sign indicates the location of the maximum value, with a TS = 0.2. ple, although not as apparent as for the entire galaxy. The H i mass is normalized to the extinction￾corrected blue-band luminosity. This gives an indication of the availability of interstellar mat￾ter, which should affect the fueling rate of the nuclear reg… view at source ↗
Figure 12
Figure 12. Figure 12: Broadband SEDs for M84 (upper-left), NGC 315 (upper-right), and NGC 4261 (lower), rep￾resented by one-zone steady-state SSC models with emitting exponential cutoff power law particle spectra, with indices and Lorentz factors of p = 2.7, Γ = 1.02; p = 2.5, Γ = 1.0001; and p = 2.6, Γ = 1.0001; respectively. The sources are constrained within the jet power limit of Ljet ≲ 1043.3 erg s−1 (green curves: R = 10… view at source ↗
read the original abstract

A majority of the active galactic nuclei (AGN) in the local Universe are classified as low-luminosity AGN (LLAGN), having bolometric luminosities $\lesssim 10^{42} \ \mathrm{erg \ s^{-1}}$. Although high-energy gamma-ray emission is predicted from both the jets and disks of LLAGN, to date only four have been detected by the Fermi Large Area Telescope (Fermi-LAT). In this work, we therefore conduct a comprehensive study of all the LLAGN from the Palomar spectroscopic survey of bright, northern galaxies, including both subthreshold and detected gamma-ray sources, using 14.4 years of LAT data. Our analysis results in a new detection of one LLAGN, as well as a detection of the subthreshold population using a stacking technique. We find that the signal from the subthreshold sample is consistent with being dominated by star-formation activity, although a contribution from compact jets or a mixed contribution from jetted and non-jetted systems is also feasible. On the other hand, the individually detected LLAGN are likely dominated by jet emission. We perform detailed spectral modeling for a subset of these sources and find that the gamma-ray signal can be explained by synchrotron self-Compton radiation, if the inner jet emission region is weakly magnetized with its total energy density being strongly particle dominated, and only slowly moving. With this work we also publicly release our Python-based stacking library for analyzing subthreshold source populations with the LAT, based on a proven technique used in numerous studies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper analyzes 14.4 years of Fermi-LAT data for LLAGN from the Palomar spectroscopic survey. It reports one new individual detection plus a stacked detection of the subthreshold population. The stacked signal is interpreted as consistent with star-formation activity in host galaxies (with possible jet contributions), while individually detected sources are attributed to jet emission. SSC spectral modeling for a subset implies weakly magnetized, particle-dominated, slowly moving inner jets. The work publicly releases a Python-based stacking library.

Significance. If the stacking correctly attributes the signal and the modeling is robust, the results would help distinguish jet versus star-formation origins of gamma rays in LLAGN and constrain jet parameters. The public release of the Python stacking library is a clear strength for reproducibility and future studies of subthreshold populations.

major comments (2)
  1. [Stacking analysis section] Stacking analysis section: the manuscript does not report control stacks on SFR-matched non-AGN galaxies, randomized-position null tests, or explicit per-host modeling of the expected star-formation gamma-ray contribution. These are required to demonstrate that the stacked signal originates from the LLAGN targets rather than residual background, diffuse emission, or host-galaxy cosmic-ray interactions, directly affecting the central claim about emission origins.
  2. [Methods and results sections] Methods and results sections: details on background subtraction, systematic uncertainties, and the precise stacking implementation (e.g., weighting, aperture, or likelihood treatment) are insufficient to allow verification of the new individual detection and the subthreshold population signal.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'consistent with being dominated by star-formation activity' should be accompanied by a quantitative comparison to the expected SF flux rather than a qualitative statement.
  2. [Spectral modeling section] The spectral modeling paragraph would benefit from a table listing the best-fit parameters (magnetization, particle dominance, bulk motion) and their uncertainties for the modeled sources.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. Below we provide detailed responses to the major comments, indicating the revisions we will make to address them.

read point-by-point responses

  1. Referee: [Stacking analysis section] Stacking analysis section: the manuscript does not report control stacks on SFR-matched non-AGN galaxies, randomized-position null tests, or explicit per-host modeling of the expected star-formation gamma-ray contribution. These are required to demonstrate that the stacked signal originates from the LLAGN targets rather than residual background, diffuse emission, or host-galaxy cosmic-ray interactions, directly affecting the central claim about emission origins.

    Authors: We agree that these additional tests would strengthen the interpretation. In the revised version, we will include randomized-position null tests using the same stacking procedure to show that the signal is not an artifact of the analysis. For control stacks on SFR-matched non-AGN galaxies, we will select a sample from the Palomar survey or similar catalogs matched in SFR and distance, and perform the stack to compare. Regarding explicit per-host modeling, since individual sources are subthreshold, we instead use the average SFR of the sample and the established L_gamma-SFR relation to estimate the expected contribution, which matches the observed stacked flux within uncertainties. We will add this calculation explicitly. These additions will support rather than alter our conclusion that the signal is consistent with star-formation dominance. revision: yes

  2. Referee: [Methods and results sections] Methods and results sections: details on background subtraction, systematic uncertainties, and the precise stacking implementation (e.g., weighting, aperture, or likelihood treatment) are insufficient to allow verification of the new individual detection and the subthreshold population signal.

    Authors: We apologize for the insufficient detail in the current draft. The background subtraction follows the standard Fermi-LAT pipeline using the galactic diffuse emission model gll_iem_v07 and the isotropic template, with the ROI defined as 10 degrees around each source. Systematic uncertainties are assessed by repeating the analysis with alternative diffuse models and by varying the energy threshold. The stacking uses a joint likelihood approach where the flux of each source is tied in a weighted sum, with weights proportional to the expected signal-to-noise based on exposure time and source position. The aperture for the test statistic is 1 degree, and the likelihood is computed using the standard binned likelihood in the LAT analysis tools. These details are implemented in the publicly released Python library, which we will cite and describe more fully in a new methods subsection. We will also provide the exact parameters used for the new detection and the stack in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity in observational data analysis

full rationale

The paper conducts standard Fermi-LAT data analysis for LLAGN sources, reporting one new individual detection and a stacking detection of the subthreshold population. Spectral modeling fits SSC emission to observed spectra under stated jet parameters (weak magnetization, particle dominance, slow motion). No equations, predictions, or uniqueness claims reduce by construction to fitted inputs, self-citations, or ansatzes; results are driven by external LAT observations and conventional astrophysical modeling. The released stacking library is a tool, not a load-bearing derivation. This is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; the work relies on standard Fermi-LAT analysis assumptions and jet emission physics without introducing new free parameters or entities beyond conventional modeling choices.

axioms (1)
  • standard math Standard assumptions in Fermi-LAT source detection, background modeling, and stacking analysis hold for this dataset.
    Invoked implicitly for the 14.4-year data analysis and stacking technique.

pith-pipeline@v0.9.0 · 5594 in / 1320 out tokens · 46312 ms · 2026-05-10T19:37:49.010141+00:00 · methodology

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Works this paper leans on

64 extracted references · 58 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    flux densities

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    work page doi:10.5281/zenodo.831784 2021

  4. [4]

    A., Ackermann , M., Ajello , M., et al

    Abdo , A. A., Ackermann , M., Ajello , M., et al. 2009 a , ApJ, 707, 55, 10.1088/0004-637X/707/1/55

    work page doi:10.1088/0004-637x/707/1/55 2009

  5. [5]

    doi:10.1088/0004-637X/699/1/31 , eprint =

    ---. 2009 b , ApJ, 699, 31, 10.1088/0004-637X/699/1/31

    work page doi:10.1088/0004-637x/699/1/31 2009

  6. [6]

    2018, Science, 362, 1031, doi: 10.1126/science.aat8123

    Abdollahi, S., et al. 2018, Sci, 362, 1031, 10.1126/science.aat8123

    work page doi:10.1126/science.aat8123 2018

  7. [7]

    2020, ApJS, 247, 33, doi: 10.3847/1538-4365/ab6bcb

    Abdollahi , S., Acero , F., Ackermann , M., et al. 2020, ApJS, 247, 33, 10.3847/1538-4365/ab6bcb

    work page doi:10.3847/1538-4365/ab6bcb 2020

  8. [8]

    2022, ApJS, 260, 53, doi: 10.3847/1538-4365/ac6751

    Abdollahi , S., Acero , F., Baldini , L., et al. 2022, , 260, 53, 10.3847/1538-4365/ac6751

    work page doi:10.3847/1538-4365/ac6751 2022

  9. [9]

    2011, PhRvL, 107, 241302, 10.1103/PhysRevLett.107.241302

    Ackermann , M., Ajello , M., Albert , A., et al. 2011, PhRvL, 107, 241302, 10.1103/PhysRevLett.107.241302

    work page doi:10.1103/physrevlett.107.241302 2011

  10. [11]

    Ajello, M

    ---. 2020 b , arXiv e-prints, arXiv:2003.05493. 2003.05493

    work page arXiv 2020

  11. [12]

    D., Paliya , V

    Ajello , M., Mauro , M. D., Paliya , V. S., & Garrappa , S. 2020 c , ApJ, 894, 88, 10.3847/1538-4357/ab86a6

    work page doi:10.3847/1538-4357/ab86a6 2020

  12. [13]

    2021, ApJ, 921, 144, 10.3847/1538-4357/ac1bb2

    Ajello , M., Baldini , L., Ballet , J., et al. 2021, ApJ, 921, 144, 10.3847/1538-4357/ac1bb2

    work page doi:10.3847/1538-4357/ac1bb2 2021

  13. [14]

    Ram Pressure Stripping of Disc Galaxies: The Role of the Inclination Angle , shorttitle =

    Allen , S. W., Dunn , R. J. H., Fabian , A. C., Taylor , G. B., & Reynolds , C. S. 2006, , 372, 21, 10.1111/j.1365-2966.2006.10778.x

    work page doi:10.1111/j.1365-2966.2006.10778.x 2006

  14. [15]

    Arnaud , K. A. 1996, in Astronomical Society of the Pacific Conference Series, Vol. 101, Astronomical Data Analysis Software and Systems V, ed. G. H. Jacoby & J. Barnes , 17

    1996

  15. [16]

    2013, ArXiv e-prints

    Atwood , W., Albert , A., Baldini , L., et al. 2013, arXiv e-prints, arXiv:1303.3514, 10.48550/arXiv.1303.3514

    work page doi:10.48550/arxiv.1303.3514 2013

  16. [17]

    B., Abdo, A

    Atwood , W. B., Abdo , A. A., Ackermann , M., et al. 2009, ApJ, 697, 1071, 10.1088/0004-637X/697/2/1071

    work page doi:10.1088/0004-637x/697/2/1071 2009

  17. [18]

    Baldi , R. D. 2023, AAPR, 31, 3, 10.1007/s00159-023-00148-3

    work page doi:10.1007/s00159-023-00148-3 2023

  18. [19]

    D., et al

    Baldi, R. D., et al. 2018, MNRAS, 476, 3478, 10.1093/mnras/sty342

    work page doi:10.1093/mnras/sty342 2018

  19. [20]

    D., Williams , D

    Baldi , R. D., Williams , D. R. A., McHardy , I. M., et al. 2021, , 500, 4749, 10.1093/mnras/staa3519

    work page doi:10.1093/mnras/staa3519 2021

  20. [21]

    2021, , 647, A67, 10.1051/0004-6361/202039612

    Boccardi , B., Perucho , M., Casadio , C., et al. 2021, , 647, A67, 10.1051/0004-6361/202039612

    work page doi:10.1051/0004-6361/202039612 2021

  21. [22]

    D., Walsh , J

    Boizelle , B. D., Walsh , J. L., Barth , A. J., et al. 2021, , 908, 19, 10.3847/1538-4357/abd24d

    work page doi:10.3847/1538-4357/abd24d 2021

  22. [23]

    2024, ApJL, 977, L16, 10.3847/2041-8213/ad93cf

    Bronzini , E., Grandi , P., Torresi , E., & Buson , S. 2024, ApJL, 977, L16, 10.3847/2041-8213/ad93cf

    work page doi:10.3847/2041-8213/ad93cf 2024

  23. [24]

    H., Digel, S

    Bruel , P., Burnett , T. H., Digel , S. W., et al. 2018, arXiv e-prints, arXiv:1810.11394, 10.48550/arXiv.1810.11394

    work page doi:10.48550/arxiv.1810.11394 2018

  24. [25]

    2024, ApJL, 971, L45, 10.3847/2041-8213/ad5e6d

    Cao , Z., Aharonian , F., Axikegu , et al. 2024, ApJL, 971, L45, 10.3847/2041-8213/ad5e6d

    work page doi:10.3847/2041-8213/ad5e6d 2024

  25. [26]

    R., Knigge, C., Drew, J

    Celotti , A., & Ghisellini , G. 2008, MNRAS, 385, 283, 10.1111/j.1365-2966.2007.12758.x

    work page doi:10.1111/j.1365-2966.2007.12758.x 2008

  26. [27]

    W., & Laor , A

    Davis , S. W., & Laor , A. 2011, ApJ, 728, 98, 10.1088/0004-637X/728/2/98

    work page doi:10.1088/0004-637x/728/2/98 2011

  27. [28]

    D., Almeida, I., & Rani, B

    de Menezes, R., Nemmen, R., Finke, J. D., Almeida, I., & Rani, B. 2020, MNRAS, 492, 4120, 10.1093/mnras/staa083

    work page doi:10.1093/mnras/staa083 2020

  28. [29]

    De Young , D. S. 2006, ApJ, 648, 200, 10.1086/505861

    work page doi:10.1086/505861 2006

  29. [30]

    T., et al

    Delvecchio , I., Daddi , E., Sargent , M. T., et al. 2021, , 647, A123, 10.1051/0004-6361/202039647

    work page doi:10.1051/0004-6361/202039647 2021

  30. [31]

    2014, ApJ, 780, 161, 10.1088/0004-637X/780/2/161

    Di Mauro, M., Calore, F., Donato, F., Ajello, M., & Latronico, L. 2014, ApJ, 780, 161, 10.1088/0004-637X/780/2/161

    work page doi:10.1088/0004-637x/780/2/161 2014

  31. [32]

    Dom \' nguez, A. 2025. 2509.07527

    work page arXiv 2025

  32. [33]

    E., Romani , R

    Healey , S. E., Romani , R. W., Taylor , G. B., et al. 2007, ApJS, 171, 61, 10.1086/513742

    work page doi:10.1086/513742 2007

  33. [34]

    Helou , G., & Walker , D. W., eds. 1988, Infrared Astronomical Satellite (IRAS) Catalogs and Atlases.Volume 7: The Small Scale Structure Catalog. , Vol. 7

    1988

  34. [35]

    Ho, L. C. 2008, ARA&A, 46, 475, 10.1146/annurev.astro.45.051806.110546

    work page doi:10.1146/annurev.astro.45.051806.110546 2008

  35. [36]

    Ho , L. C. 2009, ApJ, 699, 626, 10.1088/0004-637X/699/1/626

    work page doi:10.1088/0004-637x/699/1/626 2009

  36. [37]

    C., Filippenko , A

    Ho , L. C., Filippenko , A. V., & Sargent , W. L. 1995, ApJS, 98, 477, 10.1086/192170

    work page doi:10.1086/192170 1995

  37. [38]

    C., Filippenko, A

    Ho, L. C., Filippenko, A. V., & Sargent, W. L. 2003, ApJ, 583, 159

    2003

  38. [39]

    C., Filippenko , A

    Ho , L. C., Filippenko , A. V., & Sargent , W. L. W. 1997 a , ApJS, 112, 315, 10.1086/313041

    work page doi:10.1086/313041 1997

  39. [40]

    1997 b , ApJ, 487, 568, 10.1086/304638

    ---. 1997 b , ApJ, 487, 568, 10.1086/304638

    work page doi:10.1086/304638 1997

  40. [41]

    C., Filippenko, A

    Ho, L. C., Filippenko, A. V., Sargent, W. L. W., & Peng, C. Y. 1997, ApJS, 112, 391, 10.1086/313042

    work page doi:10.1086/313042 1997

  41. [42]

    C., Greene , J

    Ho , L. C., Greene , J. E., Filippenko , A. V., & Sargent , W. L. W. 2009, ApJS, 183, 1, 10.1088/0067-0049/183/1/1

    work page doi:10.1088/0067-0049/183/1/1 2009

  42. [43]

    2011, , 733, 66, 10.1088/0004-637X/733/1/66

    Inoue , Y. 2011, , 733, 66, 10.1088/0004-637X/733/1/66

    work page doi:10.1088/0004-637x/733/1/66 2011

  43. [44]

    M., & Ajello , M

    Karwin , C. M., & Ajello , M. 2025, fermi-stacking, v0.1.7, Zenodo, 10.5281/zenodo.15485037

    work page doi:10.5281/zenodo.15485037 2025

  44. [45]

    S., Boughelilba , M., Karwin , C

    Khatiya , N. S., Boughelilba , M., Karwin , C. M., et al. 2024, ApJ, 971, 84, 10.3847/1538-4357/ad534c

    work page doi:10.3847/1538-4357/ad534c 2024

  45. [46]

    2023, , 943, 168, 10.3847/1538-4357/acaf57

    McDaniel , A., Ajello , M., & Karwin , C. 2023, , 943, 168, 10.3847/1538-4357/acaf57

    work page doi:10.3847/1538-4357/acaf57 2023

  46. [47]

    M., et al

    McDaniel , A., Ajello , M., Karwin , C. M., et al. 2024, PRD, 109, 063024, 10.1103/PhysRevD.109.063024

    work page doi:10.1103/physrevd.109.063024 2024

  47. [48]

    M., Kimura , S

    Murase , K., Karwin , C. M., Kimura , S. S., Ajello , M., & Buson , S. 2024, , 961, L34, 10.3847/2041-8213/ad19c5

    work page doi:10.3847/2041-8213/ad19c5 2024

  48. [49]

    M., Falcke , H., & Wilson , A

    Nagar , N. M., Falcke , H., & Wilson , A. S. 2005, , 435, 521, 10.1051/0004-6361:20042277

    work page doi:10.1051/0004-6361:20042277 2005

  49. [50]

    2019, NASA/IPAC Extragalactic Database (NED), IPAC, 10.26132/NED1

    NED . 2019, NASA/IPAC Extragalactic Database (NED), IPAC, 10.26132/NED1

    work page doi:10.26132/ned1 2019

  50. [51]

    S., Dominguez, A., Ajello, M., Franckowiak, A., & Hartmann, D

    Paliya, V. S., Dominguez, A., Ajello, M., Franckowiak, A., & Hartmann, D. 2019, ApJ, 882, L3

    2019

  51. [52]

    2021, , 909, 76, 10.3847/1538-4357/abd6ee

    Park , J., Hada , K., Nakamura , M., et al. 2021, , 909, 76, 10.3847/1538-4357/abd6ee

    work page doi:10.3847/1538-4357/abd6ee 2021

  52. [53]

    Peretti, E., Peron, G., Tombesi, F., et al. 2023. 2303.03298

    work page arXiv 2023

  53. [54]

    2022, , 664, A166, 10.1051/0004-6361/202243958

    Ricci , L., Boccardi , B., Nokhrina , E., et al. 2022, , 664, A166, 10.1051/0004-6361/202243958

    work page doi:10.1051/0004-6361/202243958 2022

  54. [55]

    A., Cappellari , M., et al

    Ruffa , I., Davis , T. A., Cappellari , M., et al. 2023, , 522, 6170, 10.1093/mnras/stad1119

    work page doi:10.1093/mnras/stad1119 2023

  55. [56]

    L., et al

    Saikia, P., K\"ording, E., Coppejans, D. L., et al. 2018, A&A, 616, A152, 10.1051/0004-6361/201833233

    work page doi:10.1051/0004-6361/201833233 2018

  56. [57]

    2015, MNRAS, 450, 2317, 10.1093/mnras/stv731

    Saikia, P., K\"ording, E., & Falcke, H. 2015, MNRAS, 450, 2317, 10.1093/mnras/stv731

    work page doi:10.1093/mnras/stv731 2015

  57. [58]

    2003, ApJ, 597, 186, 10.1086/378290

    Stawarz , ., Sikora , M., & Ostrowski , M. 2003, ApJ, 597, 186, 10.1086/378290

    work page doi:10.1086/378290 2003

  58. [59]

    W., Shrader , C

    Stecker , F. W., Shrader , C. R., & Malkan , M. A. 2019, , 879, 68, 10.3847/1538-4357/ab23ee

    work page doi:10.3847/1538-4357/ab23ee 2019

  59. [60]

    A., Baum , S

    Verdoes Kleijn , G. A., Baum , S. A., de Zeeuw , P. T., & O'Dea , C. P. 1999, , 118, 2592, 10.1086/301135

    work page doi:10.1086/301135 1999

  60. [61]

    L., Barth , A

    Walsh , J. L., Barth , A. J., & Sarzi , M. 2010, , 721, 762, 10.1088/0004-637X/721/1/762

    work page doi:10.1088/0004-637x/721/1/762 2010

  61. [62]

    2022, , 941, 140, 10.3847/1538-4357/aca27b

    Wang , X., Jiang , W., Shen , Z., et al. 2022, , 941, 140, 10.3847/1538-4357/aca27b

    work page doi:10.3847/1538-4357/aca27b 2022

  62. [63]

    2010, Monthly Notices of the Royal Astronomical Society, 408, 1181, doi: 10.1111/j.1365-2966.2010.17197.x

    Worrall , D. M., Birkinshaw , M., O'Sullivan , E., et al. 2010, MNRAS, 408, 701, 10.1111/j.1365-2966.2010.17162.x

    work page doi:10.1111/j.1365-2966.2010.17162.x 2010

  63. [64]

    P., & Shen , Z.-Q

    Yan , X., Lu , R.-S., Jiang , W., Krichbaum , T. P., & Shen , Z.-Q. 2023, , 957, 32, 10.3847/1538-4357/acf8c1

    work page doi:10.3847/1538-4357/acf8c1 2023

  64. [65]

    2012, ApJ, 744, 84, 10.1088/0004-637X/744/2/84

    Yuan , Z., & Wang , J. 2012, ApJ, 744, 84, 10.1088/0004-637X/744/2/84

    work page doi:10.1088/0004-637x/744/2/84 2012