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REVIEW 4 major objections 5 minor 87 references

The AGN Optical Variability Fundamental Plane

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a two-parameter plane of AGN optical variability predicts supermassive black hole masses with 0.39 dex scatter.

desk verdict Good homogeneous-data calibration and a real improvement from adding sigma_hat, but the external validation offset means the paper overclaims precision as a mass estimator. read the letter →

arxiv 2501.12444 v1 pith:RGUYOTYH submitted 2025-01-21 astro-ph.GA

Ashley Tarrant , Jason Hinkle , Benjamin Shappee , Christopher Kochanek , Daniel Hey , Connor Auge , Anna Payne , Michael Bolish
show 5 more authors
Heechan Yuk Xinyu Dai Katie Auchettl Todd Thompson Helena Treiber
This is my paper · ORCID
classification astro-ph.GA
keywords AGNsupermassiveblackholemassesdampedrandomwalkopticalvariabilityreverberationmappingASAS-SNphotometricmassestimation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the stochastic optical flickering of an active galactic nucleus (AGN) encodes the mass of its central supermassive black hole. Using 11 years of homogeneous, near-daily ASAS-SN light curves for 57 AGN with masses measured by reverberation mapping or stellar/gas dynamics, the authors fit a damped random walk model and show that the characteristic timescale $\tau_{\rm DRW}$ alone correlates with mass, and that adding the variability amplitude $\hat{\sigma}$ tightens the relation to an intrinsic scatter of 0.39 dex. The resulting plane, Equation 2, turns photometry alone into a black hole mass estimate, which matters because reverberation mapping and dynamical measurements are expensive and limited to nearby, bright objects. The paper applies the plane to 203 bright field AGN and compares 42 overlapping sources with spectroscopic masses from the BAT AGN Spectroscopic Survey.

What carries the argument

The load-bearing object is the damped random walk (DRW) model of AGN variability, a CARMA(1,0) stochastic process characterized by a damping timescale $\tau_{\rm DRW}$ and an amplitude $\sigma$. The authors fit this model to each light curve by maximum likelihood on a grid in $\log_{10}\tau_{\rm DRW}$ and $\log_{10}\hat{\sigma}$, using $\hat{\sigma}^2 = 2\sigma^2/\tau_{\rm DRW}$ because that combination decorrelates the two fitted parameters. Because underestimated photometric errors bias $\tau_{\rm DRW}$ low, they compute per-camera, per-filter error corrections from tens of thousands of non-variable galaxy light curves and add those corrections in quadrature before fitting. The homogeneous 11-year baseline and daily cadence of ASAS-SN are what make the $\tau_{\rm DRW}$ estimates reliable across the mass range.

What would settle it

Measure $\tau_{\rm DRW}$ and $\hat{\sigma}$ for AGN with independent dynamical masses, for example megamaser disks that were not in the calibration sample, and check whether Equation 2 recovers their masses within the quoted 0.39 dex scatter; alternatively, repeat the DRW fits on light curves from a survey with independent error calibration, such as ZTF or LSST, and test whether the inferred masses agree with the ASAS-SN estimates.

Watch

Extended reading notes

Core claim

The central discovery is the calibrated variability\,--\,mass plane: $\log_{10}(M_{\rm BH}/M_\odot) = (2.27\pm0.20)\log_{10}(\tau_{\rm DRW}/200\, {\rm days}) + (1.20\pm0.20)\log_{10}(\hat{\sigma}/1\, {\rm mJy\, days^{-1/2}}) + 7.68\pm0.08$, with an intrinsic scatter of 0.39 dex. The $\tau_{\rm DRW}$-only relation already shows a significant correlation with mass (Kendall's tau of 0.50, p-value $5.0\times10^{-8}$) at 0.44 dex scatter, but the residuals correlate with $\hat{\sigma}$, so incorporating the amplitude improves the fit. The final plane shows no statistically significant residual trends with redshift or Eddington\-ratio proxy. Compared with earlier work, the homogeneous ASAS-SN light curves and the error correction yield typically longer $\tau_{\rm DRW}$ values, and the plane gives the smallest dispersion (0.70 dex) when tested against the BASS validation masses.

Load-bearing premise

The correction for underestimated ASAS-SN uncertainties, derived from quiescent galaxy light curves, is assumed to transfer to point-source AGN light curves; if that transfer is wrong, $\tau_{\rm DRW}$ and $\hat{\sigma}$ are systematically biased and the whole plane shifts.

Editorial extensions

If this is right

  • Equation 2 turns existing all-sky photometry into black hole masses for thousands of AGN; the paper reports 203 such estimates, including 60 low-mass AGN below $2\times10^6\,M_\odot$.
  • With a 25-year ASAS-SN baseline, damping timescales up to roughly 2.5 years become recoverable, corresponding to masses near $10^{9.9}\,M_\odot$, and a higher-cadence sub-survey could reach down to $10^5\,M_\odot$.
  • The 10-year LSST survey should be able to measure the plane for $7 \lesssim \log_{10}(M_{\rm BH}/M_\odot) \lesssim 9$ out to $z\sim1$ and for $\log_{10}(M_{\rm BH}/M_\odot)\sim8$ out to $z\sim4$.
  • The 0.39 dex scatter is comparable to or better than other black hole scaling relations, so photometric variability offers a competitive mass estimator where spectroscopic or dynamical data are unavailable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the plane is as stable as claimed, short-cadence surveys such as TESS or future LSST data could pre-select low-mass AGN candidates for follow-up reverberation mapping, effectively turning the mass estimator into a discovery tool.
  • The absence of a redshift trend in the calibration sample does not guarantee the plane is non-evolving; a natural extension, not pursued in the paper, would be to fit Equation 2 in redshift bins once LSST provides high-redshift AGN with independent mass anchors.
  • Because the error correction is the load-bearing step, a clean test of the paper's systematics would be to fit DRW parameters to the same AGN using a completely independent light-curve pipeline, such as forced photometry rather than image subtraction, and compare the resulting masses.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper calibrates a scaling relation between optical variability properties of AGN and SMBH mass. The authors fit damped random walk models to ~11-year ASAS-SN light curves for 57 AGN with reverberation-mapping or dynamical mass measurements, and derive Equation (2): a plane relating log M_BH to log τ_DRW and log σ̂, with an in-sample intrinsic scatter of 0.39 dex. They apply this relation to 203 bright Milliquas AGN and compare the resulting masses with BASS masses for 42 overlapping objects, finding a systematic offset of -0.53 dex and a dispersion of 0.70 dex. The paper also forecasts the reach of LSST and ASAS-SN for future variability-based mass measurements.

Significance. If the calibration were validated, this would be a valuable method for estimating SMBH masses from photometry alone, using a homogeneous, long-baseline survey dataset without the need for costly spectroscopy. The paper's strengths include the use of a homogeneous ASAS-SN dataset, careful attention to under-reported photometric uncertainties, and an explicit external-validation comparison that most similar studies omit. However, the external validation shows a large unexplained systematic offset and a dispersion nearly twice the claimed in-sample scatter, so the central claim that Equation (2) provides reliable mass estimates is not yet established. The paper is a useful calibration study, but its conclusions and abstract need to be reconciled with the validation statistics.

major comments (4)
  1. [Section 4, Figure 7, Table 4] The external validation against BASS is the decisive test of the paper's central claim, and it currently fails: the ASAS-SN masses are offset by -0.53 dex and have a 1σ dispersion of 0.70 dex after 2σ clipping that removes 10% of the sample. These numbers are reported but not explained, and the abstract and Section 5 nevertheless state that the relation provides 'reliable M_BH estimates' with a 'typical scatter of 0.39 dex'. The in-sample scatter is not the predictive accuracy of the relation when applied to field AGN. At minimum, the paper must discuss the offset, test whether it can be traced to the BASS single-epoch virial/M-σ mass scale rather than to the variability-based masses, and either recalibrate the zero point (e.g., by including a BASS-based cross-calibration term) or carefully restrict the claims to the calibration locus.
  2. [Section 2.4] The error-correction procedure is load-bearing because τ_DRW and σ̂ are derived from the flux uncertainties, and the authors themselves note that underestimated uncertainties bias τ_DRW low. The correction is calibrated on 50,000+ random galaxies, most of which are quiescent, and is then applied to AGN light curves, including strongly variable point sources. The manuscript does not demonstrate that galaxy-based corrections transfer to AGN, nor does it test the sensitivity of Equation (2) to the details of this correction (e.g., by refitting without the correction or by using the independent variable-star correction of Jayasinghe et al. 2018). A quantitative robustness test is needed to show that the plane parameters are not driven by this assumption.
  3. [Section 4 selection and clipping] The path from the initial 17,000 Milliquas sources above the variability threshold to the final 203 objects involves several cuts: the stellar-contamination cut to 1,200 sources, the removal of ~20% of sources with σ̂→0, and the restriction to 10 days < τ_DRW < 10^3.5 days. These selection effects are not modeled in the BASS comparison, and the 42 overlapping AGN may not be representative of the full field sample. In addition, the 2σ clipping removes 10% of the validation points before the offset and dispersion are computed, so the reported statistics depend on the clipping procedure. The authors should report the BASS comparison without clipping, with alternative clipping thresholds, and ideally with selection weights, to show that the validation result is robust.
  4. [Section 3 and Section 4] The paper reports the in-sample intrinsic scatter of 0.39 dex as 'the typical scatter' of the relation and compares it to other scaling relations, but this is an in-sample fit statistic, not a prediction error. The external validation yields a dispersion of 0.70 dex, which is the quantity that matters for the claimed application to field AGN. The manuscript should distinguish clearly between the two, report the external predictive scatter alongside the in-sample value in the abstract and discussion, and avoid implying that 0.39 dex is the expected accuracy of masses estimated with Equation (2) for arbitrary field objects.
minor comments (5)
  1. [Section 3] The text states that NGC 4151 is the only source with σ̂ > 40 mJy/days^1/2, but the preceding sentence says most sources have σ̂ between ~0.20 and 3.5 mJy/days^1/2; the value 40 appears to be a typo (possibly 0.40 or 4.0) and should be corrected.
  2. [Abstract] The abstract should mention the external validation offset and dispersion, or at least qualify the word 'reliable', so that readers are not misled by the in-sample scatter alone.
  3. [Equation (2)] The reported uncertainties on the two slopes and the intercept do not include the covariance between the fitted parameters; because τ_DRW and σ̂ are likely correlated, the authors should provide the covariance matrix or bootstrap uncertainties so that mass errors can be propagated correctly.
  4. [Section 5.1] The authors note in Section 5.1 that Equation (2) is assumed not to evolve with redshift, but this assumption is used to extrapolate to z~3.9 in the field sample and to forecast LSST/ASAS-SN capabilities. This important caveat should be stated earlier, in Section 4, where masses at high redshift are first presented.
  5. [Section 2.3] The choice of the 85th percentile of galaxy scatter as the variability threshold is arbitrary; at least a brief justification or a sensitivity check would help the reader assess how this selection affects the calibration sample.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Equation 2 is an empirical fit to independent reverberation-mapping and dynamical masses, and the BASS comparison is an external validation.

full rationale

The derivation chain is not circular. Equation 2 is obtained by LtsFit regression of the DRW parameters tau_DRW and sigma_hat, measured from ASAS-SN photometry, against SMBH masses taken from the independent AGN Black Hole Mass Database (reverberation mapping and dynamical measurements); the target masses are not inputs to the variability fits, and no equation defines MBH in terms of the DRW parameters. The sigma_hat term is a fitted variability parameter, not a rescaled mass. The 0.39 dex scatter is an in-sample scatter estimate obtained by adding variance in quadrature until the reduced chi2 is unity, so it is a goodness-of-fit statistic rather than an out-of-sample prediction; this limits the evidentiary strength of the precision claim but is not a circular reduction. The paper reports external validation against 42 BASS masses (Figure 7 and Table 4) with a -0.53 dex offset and 0.70 dex dispersion, which is an honest external benchmark and a correctness/calibration concern, not a self-referential input. The DRW/JAVELIN formalism is cited from standard external literature, and the ASAS-SN error correction in Section 2.4 is derived from galaxy light curves and applied before the mass regression, so it is not fitted to the target masses. No load-bearing self-citation chain, uniqueness theorem, or ansatz-smuggled-by-citation step was found, so the paper is self-contained in the circularity sense.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central relation is an empirical calibration, so the main free parameters are the fitted slopes and intercept. The DRW model and the transferability of the galaxy-based error correction are the most consequential assumptions, because any bias in tau_DRW propagates directly into the mass estimates. No new physical entities are introduced.

free parameters (5)
  • slope of log tau term in Eq. 2 = 2.27 +/- 0.20
    Fitted to the 57-object calibration sample with LtsFit; this is the coefficient of log10(tau_DRW/200 days) in the mass relation.
  • slope of log sigma_hat term in Eq. 2 = 1.20 +/- 0.20
    Fitted coefficient for log10(sigma_hat/1 mJy/days^1/2).
  • intercept in Eq. 2 = 7.68 +/- 0.08
    Fitted intercept in log10(MBH/Msun).
  • intrinsic scatter of plane = 0.39 dex
    Estimated by adding error in quadrature to the MBH uncertainties to make reduced chi^2 unity; this in-sample scatter is quoted as the predictive precision.
  • variability selection percentile = 85th percentile
    Hand-chosen percentile of galaxy light-curve scatter used to decide which AGN are variable enough for the calibration sample; affects which 86 sources become the final 57.
assumptions (4)
  • domain assumption AGN optical variability is well described by a damped random walk (DRW) process over the timescales probed.
    Invoked in Section 3; the derived tau_DRW and sigma_hat are maximum-likelihood parameters of this model, so the plane depends on the model being an adequate description.
  • domain assumption The RM and dynamical masses in the AGN Black Hole Mass Database are accurate enough to calibrate a 0.39 dex relation.
    Section 2.1; these masses are the independent variable in the fit, and their errors go into the scatter estimate.
  • ad hoc to paper Extra variance derived from galaxy light curves can be applied as quadrature corrections to AGN light curves.
    Section 2.4; the correction is fitted to non-variable galaxies and transferred to AGN; the tau values depend on it.
  • ad hoc to paper The plane relation does not evolve with redshift when applied to high-z samples.
    Section 5.1 (Figure 9); explicit assumption for future projections, not for the calibration itself.

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Cite this review

Pith. "Pith review of The AGN Optical Variability Fundamental Plane." pith.science (2026). https://pith.science/paper/RGUYOTYH

@misc{pith2026250112444,
  author       = {Pith},
  title        = {Pith review of: The AGN Optical Variability Fundamental Plane},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RGUYOTYH}},
  note         = {Machine review of arXiv:2501.12444}
}
abstract

We investigate the relationship between AGN optical variability timescales, amplitudes, and supermassive black hole (SMBH) masses using homogeneous light curves from the All-Sky Automated Survey for SuperNovae (ASAS-SN). We fit a damped random walk (DRW) model to high-cadence, long-baseline ASAS-SN light curves to estimate the characteristic variability timescale ($\tau_\text{DRW}$) and amplitude ($\sigma$) for 57 AGN with precise SMBH mass measurements from reverberation mapping and dynamical methods. We confirm a significant correlation between $\tau_\text{DRW}$ and SMBH mass, and find: $\text{log}_{10}(M_\text{BH}/ \text{M}_\odot) = (1.85\pm0.20)\times\text{log}_{10} (\tau_\text{DRW}/200 \text{ days})+7.59\pm0.08$. Incorporating $\hat{\sigma}^2 = 2\sigma^2/\tau_\text{DRW}$ in a plane model significantly improves residuals, and we find: $\text{log}_{10}(M_\text{BH}/ \text{M}_\odot) = (2.27\pm0.20)\times\text{log}_{10} (\tau_\text{DRW}/200\text{ days})+(1.20\pm0.20)\times\text{log}_{10}(\hat{\sigma}/\text{1 mJy/days}^{1/2})+7.68\pm0.08$ with a scatter of 0.39 dex. We calculate $\tau_\text{DRW}$, $\hat{\sigma}$, and estimate SMBH masses for 203 bright ($V<16$ mag) AGN from the Milliquas catalog and compare these estimates with measurements from the BAT AGN Spectroscopic Survey for 42 overlapping AGN. In 10 years, LSST could extend this method to survey $7\lesssim\text{log}_{10}({M_\text{BH}/M_\odot})\lesssim9$ SMBHs out to $z\sim1$ and $\textrm{log}_{10}({M_\text{BH}/M_\odot})\sim8.0$ out to $z\sim4$, and ASAS-SN could probe $5\lesssim \textrm{log}_{10}({M_\text{BH}/M_\odot})\lesssim10.5$ SMBHs in the local universe and $\textrm{log}_{10}({M_\text{BH}/M_\odot})\sim9.0$ out to $z\sim2$. Measuring AGN variability with these datasets will provide a unique probe of SMBH evolution by making estimates of $M_\text{BH}$ spanning several orders of magnitude with photometric observations alone.

Figures

Figures reproduced from arXiv: 2501.12444 by the authors.

Figure 1
Figure 1. Distributions of the 57 AGN that make up our final calibration sample in mass (left), redshift (center), and number of ASAS-SN epochs (right). The calibration sample consists mostly of nearby Seyfert 1 galaxies. The BH masses span more than three orders of magnitude and the median number of light curve epochs is 1,013. in 2017/18. The detectors are 20482 cooled, back-illuminated CCDs with 8. ′′0 pixels and a ∼2 pixe… view at source ↗
Figure 2
Figure 2. Selected ASAS-SN light curves of target AGN displaying high (top), typical (middle), and low (bottom) variability. Each light curve spans ∼11 years and is observed in two optical filters, 𝑉 (yellow) and 𝑔 (green). The median flux of each band has been subtracted. Low variability sources, such as the source in the bottom panel, are not included in our final calibration sample (see Section 2.3). galaxy distribution sh… view at source ↗
Figure 3
Figure 3. The intrinsic scatter in the calibration AGN light curves as a function of magnitude, compared to the scatter of randomly selected galaxies in 𝑉-band (left) and 𝑔-band (right). The orange line shows the 85th percentile light curve scatter for the random galaxies. We show all 86 AGN in our initial calibration sample, where the open triangles are the sources that were removed for not having dynamical or H𝛽 RM mass mea… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Heat maps displaying the error added in quadrature to account for underestimated ASAS-SN uncertainties based on a galaxy’s magnitude and angular distance from the ASAS-SN camera pointing center. We calculate these corrections using 50,000+ random galaxy light curves, y…
Figure 5
Figure 5. Figure 5: SMBH mass as a function of 𝜏DRW and 𝜎ˆ for the 57 final objects in the calibration sample. The circles are AGN with RM mass measurements and the squares are AGN with dynamical mass measurements from stellar or gas kinematics. We show the best-fit lines for the 2D fit u…
Figure 6
Figure 6. Figure 6: BH mass residuals from [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Mass measurements from the BAT AGN Spectroscopic Survey compared to mass estimates from this work. The color indicates the mass measurement method from the BASS survey. The open markers show the 10% of the data that are 2𝜎-clipped, where the dashed lines are the bounda…
Figure 9
Figure 9. Figure 9: The parameter space in which 𝜏DRW can be measured 12 years into the future with 25 years of ASAS-SN (blue) and 10 years of LSST (orange) 𝑔−band observations. For the higher 𝑀BH AGN, we also show a 15-day stack of the ASAS-SN survey data in green. The limiting magnitude…

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Works this paper leans on

87 extracted references · 2 canonical work pages

  1. [1]

    Alard C., 2000, @doi [ ] 10.1051/aas:2000214 , https://ui.adsabs.harvard.edu/abs/2000A&AS..144..363A 144, 363

    doi:10.1051/aas:2000214 2000

  2. [2]

    H., 1998, @doi [ ] 10.1086/305984 , https://ui.adsabs.harvard.edu/abs/1998ApJ...503..325A 503, 325

    Alard C., Lupton R. H., 1998, @doi [ ] 10.1086/305984 , https://ui.adsabs.harvard.edu/abs/1998ApJ...503..325A 503, 325

    doi:10.1086/305984 1998

  3. [3]

    Angel J. R. P., Stockman H. S., 1980, @doi [ ] 10.1146/annurev.aa.18.090180.001541 , https://ui.adsabs.harvard.edu/abs/1980ARA&A..18..321A 18, 321

    arXiv 1980

  4. [4]

    Antonucci R., 1993, @doi [ ] 10.1146/annurev.aa.31.090193.002353 , https://ui.adsabs.harvard.edu/abs/1993ARA&A..31..473A 31, 473

    arXiv 1993

  5. [5]

    Antonucci R. R. J., Miller J. S., 1985, @doi [ ] 10.1086/163559 , https://ui.adsabs.harvard.edu/abs/1985ApJ...297..621A 297, 621

    doi:10.1086/163559 1985

  6. [6]

    A., 1996, in Jacoby G

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

    1996

  7. [7]

    J., et al., 2011, @doi [ ] 10.1088/0004-637X/742/2/93 , https://ui.adsabs.harvard.edu/abs/2011ApJ...742...93A 742, 93

    Assef R. J., et al., 2011, @doi [ ] 10.1088/0004-637X/742/2/93 , https://ui.adsabs.harvard.edu/abs/2011ApJ...742...93A 742, 93

    doi:10.1088/0004-637x/742/2/93 2011

  8. [8]

    Measuring Black Hole Masses Using Ionized Gas Kinematics

    Barth A. J., Sarzi M., Ho L. C., Rix H. W., Shields J. C., Filippenko A. V., Rudnick G., Sargent W. L. W., 2001, in Knapen J. H., Beckman J. E., Shlosman I., Mahoney T. J., eds, Astronomical Society of the Pacific Conference Series Vol. 249, The Central Kiloparsec of Starbursts and AGN: The La Palma Connection. p. 370 ( @eprint arXiv astro-ph/0110672 ), @...

    work page Pith review arXiv doi:10.48550/arxiv.astro-ph/0110672 2001

Show all 87 references
  1. [9]

    C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    Bellm E. C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    2019 doi

  2. [10]

    C., Katz S., 2015, @doi [ ] 10.1086/679601 , https://ui.adsabs.harvard.edu/abs/2015PASP..127...67B 127, 67

    Bentz M. C., Katz S., 2015, @doi [ ] 10.1086/679601 , https://ui.adsabs.harvard.edu/abs/2015PASP..127...67B 127, 67

    2015 doi

  3. [11]

    C., Peterson B

    Bentz M. C., Peterson B. M., Netzer H., Pogge R. W., Vestergaard M., 2009, @doi [ ] 10.1088/0004-637X/697/1/160 , https://ui.adsabs.harvard.edu/abs/2009ApJ...697..160B 697, 160

    2009 doi

  4. [12]

    C., et al., 2013, @doi [ ] 10.1088/0004-637X/767/2/149 , https://ui.adsabs.harvard.edu/abs/2013ApJ...767..149B 767, 149

    Bentz M. C., et al., 2013, @doi [ ] 10.1088/0004-637X/767/2/149 , https://ui.adsabs.harvard.edu/abs/2013ApJ...767..149B 767, 149

    2013 doi

  5. [13]

    Bianchi S., Maiolino R., Risaliti G., 2012, @doi [Advances in Astronomy] 10.1155/2012/782030 , https://ui.adsabs.harvard.edu/abs/2012AdAst2012E..17B 2012, 782030

    2012 doi

  6. [14]

    D., McKee C

    Blandford R. D., McKee C. F., 1982, @doi [ ] 10.1086/159843 , https://ui.adsabs.harvard.edu/abs/1982ApJ...255..419B 255, 419

    1982 doi

  7. [15]

    Burke C. J., Shen Y., Chen Y.-C., Scaringi S., Faucher-Giguere C.-A., Liu X., Yang Q., 2020, @doi [ ] 10.3847/1538-4357/aba3ce , https://ui.adsabs.harvard.edu/abs/2020ApJ...899..136B 899, 136

    2020 doi

  8. [16]

    J., et al., 2021, @doi [Science] 10.1126/science.abg9933 , 373, 789

    Burke C. J., et al., 2021, @doi [Science] 10.1126/science.abg9933 , 373, 789

    2021 doi

  9. [17]

    Cappellari M., et al., 2013, @doi [ ] 10.1093/mnras/stt562 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1709C 432, 1709

    2013 doi

  10. [18]

    M., 2001, @doi [ ] 10.1086/321517 , https://ui.adsabs.harvard.edu/abs/2001ApJ...555..775C 555, 775

    Collier S., Peterson B. M., 2001, @doi [ ] 10.1086/321517 , https://ui.adsabs.harvard.edu/abs/2001ApJ...555..775C 555, 775

    2001 doi

  11. [19]

    L., Graham A

    Davis B. L., Graham A. W., Cameron E., 2018, @doi [ ] 10.3847/1538-4357/aae820 , https://ui.adsabs.harvard.edu/abs/2018ApJ...869..113D 869, 113

    2018 doi

  12. [20]

    Event Horizon Telescope Collaboration et al., 2019, @doi [ ] 10.3847/2041-8213/ab0ec7 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875L...1E 875, L1

    2019 doi

  13. [21]

    Event Horizon Telescope Collaboration et al., 2022, @doi [ ] 10.3847/2041-8213/ac6674 , https://ui.adsabs.harvard.edu/abs/2022ApJ...930L..12E 930, L12

    2022 doi

  14. [22]

    M., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6d52 , https://ui.adsabs.harvard.edu/abs/2017ApJ...840...97F 840, 97

    Fausnaugh M. M., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6d52 , https://ui.adsabs.harvard.edu/abs/2017ApJ...840...97F 840, 97

    2017 doi

  15. [23]

    Ferrarese L., Merritt D., 2000, @doi [ ] 10.1086/312838 , https://ui.adsabs.harvard.edu/abs/2000ApJ...539L...9F 539, L9

    2000 doi

  16. [24]

    W., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2105.12985 , https://ui.adsabs.harvard.edu/abs/2021arXiv210512985F p

    Flesch E. W., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2105.12985 , https://ui.adsabs.harvard.edu/abs/2021arXiv210512985F p. arXiv:2105.12985

    2021 doi arXiv

  17. [25]

    Gebhardt K., et al., 2000, @doi [ ] 10.1086/312840 , https://ui.adsabs.harvard.edu/abs/2000ApJ...539L..13G 539, L13

    2000 doi

  18. [26]

    S., 1999, @doi [ ] 10.1046/j.1365-8711.1999.02556.x , https://ui.adsabs.harvard.edu/abs/1999MNRAS.306..637G 306, 637

    Giveon U., Maoz D., Kaspi S., Netzer H., Smith P. S., 1999, @doi [ ] 10.1046/j.1365-8711.1999.02556.x , https://ui.adsabs.harvard.edu/abs/1999MNRAS.306..637G 306, 637

    1999

  19. [27]

    J., et al., 2013, @doi [ ] 10.1088/0004-637X/773/2/90 , https://ui.adsabs.harvard.edu/abs/2013ApJ...773...90G 773, 90

    Grier C. J., et al., 2013, @doi [ ] 10.1088/0004-637X/773/2/90 , https://ui.adsabs.harvard.edu/abs/2013ApJ...773...90G 773, 90

    2013 doi

  20. [28]

    J., Pancoast A., Barth A

    Grier C. J., Pancoast A., Barth A. J., Fausnaugh M. M., Brewer B. J., Treu T., Peterson B. M., 2017, @doi [ ] 10.3847/1538-4357/aa901b , https://ui.adsabs.harvard.edu/abs/2017ApJ...849..146G 849, 146

    2017 doi

  21. [29]

    G \"u ltekin K., et al., 2009, @doi [ ] 10.1088/0004-637X/698/1/198 , https://ui.adsabs.harvard.edu/abs/2009ApJ...698..198G 698, 198

    2009 doi

  22. [30]

    arXiv:2304.03791

    Hart K., et al., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2304.03791 , https://ui.adsabs.harvard.edu/abs/2023arXiv230403791H p. arXiv:2304.03791

    2023 doi arXiv

  23. [31]

    M., Collier S

    Horne K., Peterson B. M., Collier S. J., Netzer H., 2004, @doi [ ] 10.1086/420755 , https://ui.adsabs.harvard.edu/abs/2004PASP..116..465H 116, 465

    2004 doi

  24. [32]

    Ivezi \'c Z ., et al., 2019, @doi [ ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    2019 doi

  25. [33]

    Jayasinghe T., et al., 2018, @doi [ ] 10.1093/mnras/sty838 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.3145J 477, 3145

    2018 doi

  26. [34]

    S., Netzer H., Maoz D., Jannuzi B

    Kaspi S., Smith P. S., Netzer H., Maoz D., Jannuzi B. T., Giveon U., 2000, @doi [ ] 10.1086/308704 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533..631K 533, 631

    2000 doi

  27. [35]

    M., Vestergaard M., Jannuzi B

    Kaspi S., Maoz D., Netzer H., Peterson B. M., Vestergaard M., Jannuzi B. T., 2005, @doi [ ] 10.1086/431275 , https://ui.adsabs.harvard.edu/abs/2005ApJ...629...61K 629, 61

    2005 doi

  28. [36]

    L., 1998, @doi [The Astrophysical Journal] 10.1086/306105 , 504, 671

    Kawaguchi T., Mineshige S., Umemura M., Turner E. L., 1998, @doi [The Astrophysical Journal] 10.1086/306105 , 504, 671

    1998 doi

  29. [37]

    C., Bechtold J., Siemiginowska A., 2009, @doi [ ] 10.1088/0004-637X/698/1/895 , https://ui.adsabs.harvard.edu/abs/2009ApJ...698..895K 698, 895

    Kelly B. C., Bechtold J., Siemiginowska A., 2009, @doi [ ] 10.1088/0004-637X/698/1/895 , https://ui.adsabs.harvard.edu/abs/2009ApJ...698..895K 698, 895

    2009 doi

  30. [38]

    C., Becker A

    Kelly B. C., Becker A. C., Sobolewska M., Siemiginowska A., Uttley P., 2014, @doi [ ] 10.1088/0004-637X/788/1/33 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...33K 788, 33

    2014 doi

  31. [39]

    S., et al., 2017, @doi [ ] 10.1088/1538-3873/aa80d9 , https://ui.adsabs.harvard.edu/abs/2017PASP..129j4502K 129, 104502

    Kochanek C. S., et al., 2017, @doi [ ] 10.1088/1538-3873/aa80d9 , https://ui.adsabs.harvard.edu/abs/2017PASP..129j4502K 129, 104502

    2017 doi

  32. [40]

    C., 2013, @doi [ ] 10.1146/annurev-astro-082708-101811 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..511K 51, 511

    Kormendy J., Ho L. C., 2013, @doi [ ] 10.1146/annurev-astro-082708-101811 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..511K 51, 511

    2013 doi

  33. [41]

    Kormendy J., Richstone D., 1995, @doi [ ] 10.1146/annurev.aa.33.090195.003053 , https://ui.adsabs.harvard.edu/abs/1995ARA&A..33..581K 33, 581

    1995

  34. [42]

    J., et al., 2022a, @doi [ ] 10.3847/1538-4365/ac6c05 , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....2K 261, 2

    Koss M. J., et al., 2022a, @doi [ ] 10.3847/1538-4365/ac6c05 , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....2K 261, 2

    doi

  35. [43]

    J., et al., 2022b, @doi [ ] 10.3847/1538-4365/ac650b , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....6K 261, 6

    Koss M. J., et al., 2022b, @doi [ ] 10.3847/1538-4365/ac650b , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....6K 261, 6

    doi

  36. [44]

    Koz owski S., et al., 2010, @doi [ ] 10.1088/0004-637X/708/2/927 , https://ui.adsabs.harvard.edu/abs/2010ApJ...708..927K 708, 927

    2010 doi

  37. [45]

    Kozłowski S., 2021, @doi [Acta Astronomica] 10.32023/0001-5237/71.2.2 , 71, 103–112

    2021 doi

  38. [46]

    A., et al., 2013, @doi [ ] 10.1088/0067-0049/209/1/14 , https://ui.adsabs.harvard.edu/abs/2013ApJS..209...14K 209, 14

    Krimm H. A., et al., 2013, @doi [ ] 10.1088/0067-0049/209/1/14 , https://ui.adsabs.harvard.edu/abs/2013ApJS..209...14K 209, 14

    2013 doi

  39. [47]

    Kubota A., Done C., 2018, @doi [ ] 10.1093/mnras/sty1890 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.1247K 480, 1247

    2018 doi

  40. [48]

    L., et al., 2010, @doi [ ] 10.1088/0004-637X/721/2/1014 , https://ui.adsabs.harvard.edu/abs/2010ApJ...721.1014M 721, 1014

    MacLeod C. L., et al., 2010, @doi [ ] 10.1088/0004-637X/721/2/1014 , https://ui.adsabs.harvard.edu/abs/2010ApJ...721.1014M 721, 1014

    2010 doi

  41. [49]

    L., et al., 2012, @doi [ ] 10.1088/0004-637X/753/2/106 , https://ui.adsabs.harvard.edu/abs/2012ApJ...753..106M 753, 106

    MacLeod C. L., et al., 2012, @doi [ ] 10.1088/0004-637X/753/2/106 , https://ui.adsabs.harvard.edu/abs/2012ApJ...753..106M 753, 106

    2012 doi

  42. [50]

    Magorrian J., et al., 1998, @doi [ ] 10.1086/300353 , https://ui.adsabs.harvard.edu/abs/1998AJ....115.2285M 115, 2285

    1998 doi

  43. [51]

    Makarov D., Prugniel P., Terekhova N., Courtois H., Vauglin I., 2014, @doi [ ] 10.1051/0004-6361/201423496 , https://ui.adsabs.harvard.edu/abs/2014A&A...570A..13M 570, A13

    2014 doi

  44. [52]

    J., Ma C.-P., Murphy J

    McConnell N. J., Ma C.-P., Murphy J. D., Gebhardt K., Lauer T. R., Graham J. R., Wright S. A., Richstone D. O., 2012, @doi [ ] 10.1088/0004-637X/756/2/179 , https://ui.adsabs.harvard.edu/abs/2012ApJ...756..179M 756, 179

    2012 doi

  45. [53]

    J., Jarvis M

    McLure R. J., Jarvis M. J., 2002, @doi [ ] 10.1046/j.1365-8711.2002.05871.x , https://ui.adsabs.harvard.edu/abs/2002MNRAS.337..109M 337, 109

    2002

  46. [54]

    E., et al., 2022, @doi [ ] 10.3847/1538-4365/ac6602 , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....5M 261, 5

    Mej \' a-Restrepo J. E., et al., 2022, @doi [ ] 10.3847/1538-4365/ac6602 , https://ui.adsabs.harvard.edu/abs/2022ApJS..261....5M 261, 5

    2022 doi

  47. [55]

    Netzer H., 2015, @doi [ ] 10.1146/annurev-astro-082214-122302 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53..365N 53, 365

    2015 doi

  48. [56]

    Neustadt J. M. M., Kochanek C. S., 2022, @doi [ ] 10.1093/mnras/stac888 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513.1046N 513, 1046

    2022 doi

  49. [57]

    Neustadt J. M. M., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1386 , https://ui.adsabs.harvard.edu/abs/2024ApJ...961..219N 961, 219

    2024 doi

  50. [58]

    D., Thorne K

    Novikov I. D., Thorne K. S., 1973, in Dewitt C., Dewitt B. S., eds, Black Holes (Les Astres Occlus). pp 343--450

    1973

  51. [59]

    A., Kollmeier J

    Onken C. A., Kollmeier J. A., 2008, @doi [ ] 10.1086/595746 , https://ui.adsabs.harvard.edu/abs/2008ApJ...689L..13O 689, L13

    2008 doi

  52. [60]

    A., Ferrarese L., Merritt D., Peterson B

    Onken C. A., Ferrarese L., Merritt D., Peterson B. M., Pogge R. W., Vestergaard M., Wandel A., 2004, @doi [ ] 10.1086/424655 , https://ui.adsabs.harvard.edu/abs/2004ApJ...615..645O 615, 645

    2004 doi

  53. [61]

    M., Wanders I., Bertram R., Hunley J

    Peterson B. M., Wanders I., Bertram R., Hunley J. F., Pogge R. W., Wagner R. M., 1998, @doi [ ] 10.1086/305813 , https://ui.adsabs.harvard.edu/abs/1998ApJ...501...82P 501, 82

    1998 doi

  54. [62]

    M., et al., 2004, @doi [ ] 10.1086/423269 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..682P 613, 682

    Peterson B. M., et al., 2004, @doi [ ] 10.1086/423269 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..682P 613, 682

    2004 doi

  55. [63]

    H., Rybicki G

    Press W. H., Rybicki G. B., Hewitt J. N., 1992, @doi [ ] 10.1086/170951 , https://ui.adsabs.harvard.edu/abs/1992ApJ...385..404P 385, 404

    1992 doi

  56. [64]

    J., 1966, @doi [ ] 10.1038/211468a0 , https://ui.adsabs.harvard.edu/abs/1966Natur.211..468R 211, 468

    Rees M. J., 1966, @doi [ ] 10.1038/211468a0 , https://ui.adsabs.harvard.edu/abs/1966Natur.211..468R 211, 468

    1966 doi

  57. [65]

    J., 1988, @doi [ ] 10.1038/333523a0 , https://ui.adsabs.harvard.edu/abs/1988Natur.333..523R 333, 523

    Rees M. J., 1988, @doi [ ] 10.1038/333523a0 , https://ui.adsabs.harvard.edu/abs/1988Natur.333..523R 333, 523

    1988 doi

  58. [66]

    R., et al., 2014, in Oschmann Jacobus M

    Ricker G. R., et al., 2014, in Oschmann Jacobus M. J., Clampin M., Fazio G. G., MacEwen H. A., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 9143, Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave. p. 9143...

    2014 arXiv

  59. [67]

    B., Press W

    Rybicki G. B., Press W. H., 1992, @doi [ ] 10.1086/171845 , https://ui.adsabs.harvard.edu/abs/1992ApJ...398..169R 398, 169

    1992 doi

  60. [68]

    P., et al., 2016, @doi [ ] 10.3847/0004-637X/818/1/47 , https://ui.adsabs.harvard.edu/abs/2016ApJ...818...47S 818, 47

    Saglia R. P., et al., 2016, @doi [ ] 10.3847/0004-637X/818/1/47 , https://ui.adsabs.harvard.edu/abs/2016ApJ...818...47S 818, 47

    2016 doi

  61. [69]

    W., Davis B

    Sahu N., Graham A. W., Davis B. L., 2019, @doi [ ] 10.3847/1538-4357/ab0f32 , https://ui.adsabs.harvard.edu/abs/2019ApJ...876..155S 876, 155

    2019 doi

  62. [70]

    J., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/48 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...48S 788, 48

    Shappee B. J., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/48 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...48S 788, 48

    2014 doi

  63. [71]

    Siemiginowska A., Czerny B., 1989, @doi [ ] 10.1093/mnras/239.1.289 , https://ui.adsabs.harvard.edu/abs/1989MNRAS.239..289S 239, 289

    1989 doi

  64. [72]

    S., Jeyakumar S., Coziol R., Pawase R

    Stalin C. S., Jeyakumar S., Coziol R., Pawase R. S., Thakur S. S., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19030.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.416..225S 416, 225

    2011

  65. [73]

    L., Ivezi \'c Z ., MacLeod C., 2021, @doi [ ] 10.3847/1538-4357/abc698 , https://ui.adsabs.harvard.edu/abs/2021ApJ...907...96S 907, 96

    Suberlak K. L., Ivezi \'c Z ., MacLeod C., 2021, @doi [ ] 10.3847/1538-4357/abc698 , https://ui.adsabs.harvard.edu/abs/2021ApJ...907...96S 907, 96

    2021 doi

  66. [74]

    L., et al., 2018a, @doi [ ] 10.1088/1538-3873/aabadf , https://ui.adsabs.harvard.edu/abs/2018PASP..130f4505T 130, 064505

    Tonry J. L., et al., 2018a, @doi [ ] 10.1088/1538-3873/aabadf , https://ui.adsabs.harvard.edu/abs/2018PASP..130f4505T 130, 064505

    doi

  67. [75]

    L., et al., 2018b, @doi [ ] 10.3847/1538-4357/aae386 , https://ui.adsabs.harvard.edu/abs/2018ApJ...867..105T 867, 105

    Tonry J. L., et al., 2018b, @doi [ ] 10.3847/1538-4357/aae386 , https://ui.adsabs.harvard.edu/abs/2018ApJ...867..105T 867, 105

    doi

  68. [76]

    P., et al., 2023, @doi [ ] 10.1093/mnras/stad2530 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.5795T 525, 5795

    Treiber H. P., et al., 2023, @doi [ ] 10.1093/mnras/stad2530 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.5795T 525, 5795

    2023 doi

  69. [77]

    M., 1997, @doi [ ] 10.1146/annurev.astro.35.1.445 , https://ui.adsabs.harvard.edu/abs/1997ARA&A..35..445U 35, 445

    Ulrich M.-H., Maraschi L., Urry C. M., 1997, @doi [ ] 10.1146/annurev.astro.35.1.445 , https://ui.adsabs.harvard.edu/abs/1997ARA&A..35..445U 35, 445

    1997 doi

  70. [78]

    M., Padovani P., 1995, @doi [ ] 10.1086/133630 , https://ui.adsabs.harvard.edu/abs/1995PASP..107..803U 107, 803

    Urry C. M., Padovani P., 1995, @doi [ ] 10.1086/133630 , https://ui.adsabs.harvard.edu/abs/1995PASP..107..803U 107, 803

    1995 doi

  71. [79]

    E., et al., 2004, @doi [ ] 10.1086/380563 , https://ui.adsabs.harvard.edu/abs/2004ApJ...601..692V 601, 692

    Vanden Berk D. E., et al., 2004, @doi [ ] 10.1086/380563 , https://ui.adsabs.harvard.edu/abs/2004ApJ...601..692V 601, 692

    2004 doi

  72. [80]

    Vestergaard M., 2002, @doi [ ] 10.1086/340045 , https://ui.adsabs.harvard.edu/abs/2002ApJ...571..733V 571, 733

    2002 doi

  73. [81]

    M., 2006, @doi [ ] 10.1086/500572 , https://ui.adsabs.harvard.edu/abs/2006ApJ...641..689V 641, 689

    Vestergaard M., Peterson B. M., 2006, @doi [ ] 10.1086/500572 , https://ui.adsabs.harvard.edu/abs/2006ApJ...641..689V 641, 689

    2006 doi

  74. [82]

    D., Kochanek C

    Yuk H., Dai X., Jayasinghe T., Fu H., Mishra H. D., Kochanek C. S., Shappee B. J., Stanek K. Z., 2022, @doi [ ] 10.3847/1538-4357/ac6423 , https://ui.adsabs.harvard.edu/abs/2022ApJ...930..110Y 930, 110

    2022 doi

  75. [83]

    S., Peterson B

    Zu Y., Kochanek C. S., Peterson B. M., 2011, @doi [ ] 10.1088/0004-637X/735/2/80 , https://ui.adsabs.harvard.edu/abs/2011ApJ...735...80Z 735, 80

    2011 doi

  76. [84]

    S., Koz owski S., Udalski A., 2013, @doi [ ] 10.1088/0004-637X/765/2/106 , https://ui.adsabs.harvard.edu/abs/2013ApJ...765..106Z 765, 106

    Zu Y., Kochanek C. S., Koz owski S., Udalski A., 2013, @doi [ ] 10.1088/0004-637X/765/2/106 , https://ui.adsabs.harvard.edu/abs/2013ApJ...765..106Z 765, 106

    2013 doi

  77. [85]

    S., Koz owski S., Peterson B

    Zu Y., Kochanek C. S., Koz owski S., Peterson B. M., 2016, @doi [ ] 10.3847/0004-637X/819/2/122 , https://ui.adsabs.harvard.edu/abs/2016ApJ...819..122Z 819, 122

    2016 doi

  78. [86]

    de Jaeger T., et al., 2023, @doi [ ] 10.1093/mnras/stad060 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.6349D 519, 6349

    2023 doi

  79. [87]

    van den Bosch R. C. E., 2016, @doi [ ] 10.3847/0004-637X/831/2/134 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831..134V 831, 134

    2016 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.