Pith. sign in

REVIEW 3 major objections 4 minor 54 references

No sign of G2's encounter affecting Sgr A*'s X-ray flaring rate from $Chandra$ observations

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read A Chandra-only reanalysis of Sgr A* finds no change in the X-ray flaring rate around G2's 2014 pericenter passage, undercutting earlier claims of a post-G2 bright-flare surge.

desk verdict Careful Chandra-only reanalysis of the G2 flare-rate question, but the central '95% confidence' null claim is not supported by the 70% Monte Carlo envelopes used in the test. read the letter →

arxiv 1909.02175 v1 pith:CGHYTTDG submitted 2019-09-05 astro-ph.GA astro-ph.HE

Elie Bouffard , Daryl Haggard , Michael A. Nowak , Joseph Neilsen , Sera Markoff , Frederick K. Baganoff This is my paper · ORCID
classification astro-ph.GAastro-ph.HE
keywords SgrA*X-rayflaresG2cloudChandraObservatoryGalacticCentreBayesianBlocksflareratemagnetarcontamination
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 tests whether the close passage of the gas cloud G2 past Sgr A*, the Milky Way's supermassive black hole, in summer 2014 increased the black hole's X-ray flaring rate. Using 4.5 Ms of Chandra observations from 2012 to 2018 and Monte Carlo simulations that model flares, instrument modes, pile-up, and contamination from a nearby magnetar, the authors find that the same model parameters reproduce the observed flare energy distributions before and after every plausible change point. They therefore fail to reject the null hypothesis of a constant flaring rate at more than 95% confidence. If right, earlier reports of a post-G2 surge in bright X-ray flares are artifacts of combining data from multiple observatories with different sensitivities or of short-term flare clustering, not a real response of the accretion flow.

What carries the argument

The load-bearing machinery is a null-hypothesis Monte Carlo simulation of complete Chandra light curves. Flares are placed at Poisson-random times, given Gaussian shapes with durations and emitted energies drawn from power-law distributions, subjected to pile-up and instrument-mode corrections, and then run through the same Bayesian Blocks detection pipeline as the real data; the same model parameters must reproduce the flare energy distributions of both datasets around each candidate change point. A second piece is the magnetar contamination correction: the leakage fraction $\epsilon\approx(1.3\pm0.2)\%$ of SGR J1745-2900's count rate into Sgr A*'s 1.25 arcsec extraction region is measured per observation, so quiescent count rates can be estimated correctly. Bayesian Blocks with a calibrated false-positive prior $p_0=0.05$ identifies the flares.

What would settle it

Count the bright flares (unabsorbed energy above about $9.2\times10^{37}$ erg) in the 2016-2018 Chandra observations and compare with the 2012 XVP rate using a null model that allows Poisson clustering on 20-70 ks timescales; finding a rate above about 1.2 flares per day at 95% confidence would reject the paper's null. A simpler check is to re-run the 2014 change-point test using only flares detected in the zeroth order of both datasets, the mode where the previously reported bright surge is claimed to be absent.

Watch

Extended reading notes

Core claim

On the authors' own terms, the central finding is that Sgr A*'s bright X-ray flaring rate shows no statistically significant change near G2's pericenter passage. They detect 58 flares using Bayesian Blocks in Chandra data split between the 2012 X-ray Visionary Program (3 Ms, HETG gratings mode) and a homogeneous Post-XVP sample (1.56 Ms, ACIS-S 1/8th subarray, 2013-2018), after correcting for the time-variable leakage of the magnetar SGR J1745-2900 into the Sgr A* extraction region (measured leakage fraction $(1.3\pm0.2)\%$). Their Monte Carlo model, which places Gaussian flares drawn from power-law energy and duration distributions at Poisson times and applies the same detection pipeline to simulated and real events, produces 70% confidence bands that match the observed energy distributions for XVP and Post-XVP data split at four candidate change points in 2014, including 2014 April 4 and August 30. The bright flaring rates are consistent between epochs ($0.29\pm0.09$ versus $0.3\pm0.1$ flares per day), and the authors conclude there is no evidence for a change point above 95% confidence.

Load-bearing premise

The Monte Carlo null test assumes Sgr A*'s flares occur at random, independent Poisson times; if flares actually cluster on timescales of tens of kiloseconds (as earlier work suggests), the simulated 70% confidence intervals are too narrow, so failing to reject the null may reflect the model rather than a truly constant rate.

Editorial extensions

If this is right

  • The previously reported post-G2 increase in bright Sgr A* X-ray flares is not reproduced in a Chandra-only sample; if the claim is correct, that increase was driven by detector cross-calibration effects or by a few bright flares seen only by XMM-Newton.
  • The intrinsic X-ray flaring rate of Sgr A* was statistically constant near the 1.0-1.2 flares per day level across 2012-2018, implying G2's passage did not, at least yet, change the accretion flow's flaring behaviour.
  • The failure to find a change constrains models that predict G2-induced activity on timescales of a few years; a delayed rise after 2018, for example 5-10 years post-pericenter, remains a live possibility that continued monitoring can test.
  • The analysis shows that instrument mode, pile-up, edge effects, and a contaminating point source can all masquerade as a flaring-rate change, so single-observatory, homogeneously reduced datasets are needed for this kind of comparison.

Reading between the lines

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

  • If Sgr A*'s flares genuinely cluster on 20-70 ks timescales, as the paper notes earlier work found, then the Poisson-based confidence intervals used here are too narrow; a clustering-aware simulation would likely make the null of constant rate even harder to reject, meaning the test's power to detect a real G2-driven burst is limited.
  • The paper's conclusion is about the distribution of emitted flare energies and their rate; it does not test whether G2 changed flare spectra, durations, or the correlation between X-ray and near-infrared variability, so those channels remain open for future joint monitoring.
  • A testable extension is to apply the same simulation pipeline to a change point placed not in 2014 but in 2017-2018, where viscous-timescale accretion models predict a delayed G2 signature; the current dataset already contains some 2016-2018 exposure and could be re-split there.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper reanalyzes Chandra observations of Sgr A* from 2012 to 2018 to test whether the flaring rate or flare energy distribution changed around G2's 2014 pericenter passage. Using Bayesian Blocks to detect and characterize flares, the authors build Monte Carlo simulations of the XVP and Post-XVP light curves with a single set of model parameters and compare the observed binned flare energy distributions to 70% simulation envelopes. They report consistency between pre- and post-G2 datasets for several candidate change points, and conclude that there is no evidence of a change point in the energy distribution above 95% confidence, contradicting earlier claims by Ponti et al. (2015) and Mossoux & Grosso (2017).

Significance. If the central result holds, the paper provides an important counterpoint to previously reported increases in Sgr A*'s bright X-ray flaring rate after G2's encounter, and it supplies a carefully constructed Chandra-only dataset with detailed treatment of magnetar contamination and instrument-mode differences. The work has several genuine strengths: the Bayesian Blocks calibration on signal-free light curves, the explicit modeling of pile-up and instrument responses, the systematic comparison with previous flare catalogs, and the release of simulation code. However, the central statistical claim is currently overstated relative to the method used, and the Poisson assumption for flare times is acknowledged as questionable; these issues are load-bearing for the paper's main conclusion.

major comments (3)
  1. [§4, Figs. 6 and 8; §6 Conclusion] The conclusion in Section 6 that there is "no evidence of a change point in the energy distribution above 95% confidence" is not supported by the Monte Carlo intervals constructed in Section 4. The simulations produce 15%–85% (70%) confidence envelopes, and the consistency check is whether observed bins fall inside those envelopes. Under the null hypothesis, 30% of simulated datasets fall outside the shaded region in any given bin; a 70% interval therefore cannot license a 95% confidence statement. The paper should either recompute the envelopes at 95% (or another explicitly justified level) or revise the wording in the abstract and conclusion to describe consistency within 70% envelopes, with the corresponding caveats.
  2. [§4.1, §4.2] The statistical test is a bin-by-bin comparison with no global test statistic and no p-value or power calculation. The model parameters in Section 4.1 (Gamma_Dura = -0.8, Gamma_Energy = -1.7, the energy and duration ranges, and the intrinsic flaring rate of 52 flares per 3 Ms) are selected by trial and error, and consistency is assessed by eye from the overlap of binned data with simulation envelopes. As written, "failing to reject the null hypothesis" does not quantify how unlikely the data would be under the null, nor how much rate increase the test could actually detect. A formal global statistic (for example, a likelihood-ratio or sum-of-chi-squares over bins) and a power calculation against the rate increase claimed by Ponti et al. (2015) are needed to make the null claim quantitative.
  3. [§5] The Poisson assumption for flare arrival times is acknowledged by the authors to be potentially violated, with Yuan & Wang (2016) reporting flare clustering on 20–70 ks timescales at 96% significance. Because clustering broadens the sampling variability of count-based statistics, simulated Poisson-based confidence intervals are likely to be too narrow, so the failure to reject the null could be an artifact of the model rather than evidence for a constant rate. This is not a minor caveat; it directly affects the central claim. The paper should test robustness by simulating clustered flare times (for example, through a variable-rate Poisson process or the piecewise-deterministic Markov process mentioned in Section 5) or otherwise demonstrate that the conclusion is insensitive to clustering.
minor comments (4)
  1. [Figure 3 caption] The caption reads "Qeff × Qmagn" where the text and Table 1 indicate the quantity plotted is Qsgr = Qeff - ⟨ϵ⟩ Qmagn; please correct the label.
  2. [§5 / §6] The sentence "The 3σ upper limit of 3 is 9 giving a rate of 1.2 flare day-1" is unclear and should be rephrased to state explicitly what quantity is bounded.
  3. [Table 4 header, §1] There are minor typographical issues, including "obervations" in Table 4 and "peripassage" in the introduction; please proofread the manuscript.
  4. [References] Several references are cited as in-preparation or preprint (e.g., Haggard et al. 2019; Gillessen et al. 2018); the authors should update these if published versions exist by the time of resubmission.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the Monte Carlo null test is self-contained; minor self-citations are cross-checks, not load-bearing. The main caveat is statistical (70% intervals are quoted as a 95% conclusion), not circular.

full rationale

This paper is an observational consistency test, not a derivation from first principles. The authors detect flares with Bayesian Blocks, build energy and duration distributions, and then simulate synthetic Chandra datasets from a Monte Carlo model with stated parameters (power-law indices, energy range, flaring rate, quiescent rates, pile-up). The null hypothesis is that the same model parameters can reproduce the pre- and post-change-point distributions; the test is whether the observed binned distributions fall inside simulated 70% envelopes. The simulation parameters in Section 4.1 are admittedly 'found by trial and error' and fitted to the aggregate XVP and Post-XVP datasets, but the target of the test is not a numerical value predicted from that fit; it is consistency of split subsets under a single set of parameters. A consistency check of this kind can fail, and indeed earlier multi-observatory analyses claimed a rate increase that this Chandra-only analysis does not recover. No equation in the paper defines X in terms of Y or renames a fitted parameter as a prediction. Self-citations to Nowak et al. (2012), Neilsen et al. (2013), and co-authored prior work are used for cross-checks (quiescent count rate, spectral conversion, flare-rate comparison, pile-up model), not to forbid alternatives or to supply the null result. The paper even flags its own limitations: the Poisson-time assumption may be wrong given Yuan & Wang (2016) clustering evidence, and the model parameters 'may not represent the true physical parameters.' One non-circular but important weakness is flagged: Section 4 states that 3000 simulations produce '15% - 85% (70% intervals) confidence intervals for each bin,' while Section 6 concludes there is 'no evidence of a change point in the energy distribution above 95% confidence.' A 70% interval is not a 95% test, so the conclusion overstates the confidence level of the null result; this is a statistical calibration or reporting problem, not a circular derivation. Overall circularity is minimal, meriting a score of 1 for the presence of minor, non-load-bearing self-citations.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The analysis depends on a set of simulation parameters tuned by trial and error (power-law indices, ranges, flaring rate, Gaussian width, significance threshold) and on domain assumptions about flare spectra, Poisson timing, and constant quiescence. No new physical entities are introduced. The paper's null test is only as reliable as these assumptions, several of which are contested by the cited literature (e.g., flare clustering).

free parameters (7)
  • Energy distribution power-law index Γ_Energy = -1.7
    Chosen by trial and error to reproduce the observed combined flare energy distribution (Section 4.1). The paper states these may not be the true physical parameters.
  • Duration distribution power-law index Γ_Dura = -0.8
    Chosen by trial and error alongside Γ_Energy in Section 4.1.
  • Simulated duration range = 500 to 8000 s
    Set to cover the observed range of flare durations, as stated in Section 4.1.
  • Simulated energy range = 1.3e37 to 275e37 erg
    Based on observed flares, e.g., the most energetic flare in ObsID 15043; Section 4.1.
  • Intrinsic flaring rate = 52 flares per 3 Ms (~1.5 per day)
    Chosen by trial and error; higher than the observed rate because it represents the intrinsic rate before detection losses; Section 4.1.
  • Gaussian width factor (duration = 4σ) = 4
    The paper tried other values and selected 4 as 'the most reliable' using simulated flares; Section 4.
  • Flaring block significance threshold = 3 (σ_Q + σ_block)
    Chosen by trial and error in Section 3.4; the paper compared quadrature addition and lower thresholds.
assumptions (7)
  • domain assumption Sgr A* flare spectra follow a power law with photon index Γ=2 and the stated absorption parameters.
    Used to convert count rates to energies (Section 3.3), based on prior works (Nowak et al. 2012, Neilsen et al. 2013).
  • ad hoc to paper Flares have Gaussian temporal profiles with standard deviation equal to duration/4.
    Section 4; the factor 4 was chosen by trial and error to make simulations behave like data.
  • domain assumption Flare emission times follow a Poisson process with no clustering.
    Section 5 explicitly assumes Poisson times and notes that Yuan & Wang (2016) found clustering at 96% significance on 20-70 ks timescales.
  • ad hoc to paper Flare energies and durations are drawn from power-law distributions that are the same before and after any change point.
    This is the null model being tested; the power-law shape and the same-parameter requirement are assumed in Section 4.
  • domain assumption The magnetar contamination fraction ε is constant over time with a mean of 1.3% after pile-up correction.
    Derived from measurements in Section 2.3; no long-term trend was seen, so a global mean is used.
  • standard math Bayesian Blocks prior calibration for Poisson noise yields a 5% false-positive rate per change point.
    Calibrated via signal-free simulations in Appendix A; this is a standard method.
  • domain assumption The quiescent count rate is determined from the longest Bayesian Block in each observation and is constant during that observation.
    Section 3.4; the quiescent rate is needed to define flaring blocks.

how reviews work

0 comments
Cite this review

Pith. "Pith review of No sign of G2's encounter affecting Sgr A*'s X-ray flaring rate from $Chandra$ observations." pith.science (2026). https://pith.science/paper/CGHYTTDG

@misc{pith2026190902175,
  author       = {Pith},
  title        = {Pith review of: No sign of G2's encounter affecting Sgr A*'s X-ray flaring rate from $Chandra$ observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CGHYTTDG}},
  note         = {Machine review of arXiv:1909.02175}
}
abstract

An unusual object, G2, had its pericenter passage around Sgr A*, the $4\times10^6$ M$_\odot$ supermassive black hole in the Galactic Centre, in Summer 2014. Several research teams have reported evidence that following G2's pericenter encounter the rate of Sgr A*'s bright X-ray flares increased significantly. Our analysis carefully treats varying flux contamination from a nearby magnetic neutron star and is free from complications induced by using data from multiple X-ray observatories with different spatial resolutions. We test the scenario of an increased bright X-ray flaring rate using a massive dataset from the \textit{Chandra X-ray Observatory}, the only X-ray instrument that can spatially distinguish between Sgr A* and the nearby Galactic Centre magnetar throughout the full extended period encompassing G2's encounter with Sgr A*. We use X-ray data from the 3 Ms observations of the \textit{Chandra} \textit{X-ray Visionary Program} (XVP) in 2012 as well as an additional 1.5 Ms of observations up to 2018. We use detected flares to make distributions of flare properties. Using simulations of X-ray flares accounting for important factors such as the different $Chandra$ instrument modes, we test the null hypothesis on Sgr A*'s bright (or any flare category) X-ray flaring rate around different potential change points. In contrast to previous studies, our results are consistent with the null hypothesis; the same model parameters produce distributions consistent with the observed ones around any plausible change point.

Figures

Figures reproduced from arXiv: 1909.02175 by the authors.

Figure 1
Figure 1. T op: Image of ObsID 14703 (2013 June 4) from Chandra. The annulus is the background extraction region used for each source (inner radius of 5" and outer radius of 8", center on RA:17h 45m40s .084, DEC:-29◦ 00’ 28.70" ). The brightest source is the magnetar (extraction region centered on RA:17h 45m40s .169, DEC:-29◦ 00’ 29.84" with a radius of 1.3"). The dashed circle towards the upper-right from the magnetar is the… view at source ↗
Figure 3
Figure 3. We plot the difference between Qeff and hi × Qmagn in blue and the quiescent count rate predicted by Nowak et al. (2012) in red, QNowak, at each ObsID present in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Like [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Light curve of ObsID 15045 (2013 October 28). The bin time is 300 s, the Bayesian Blocks are in red with their associated Poisson errors, the rightmost block is the quiescent block and the blue vertical bars indicate the beginning and end times of the two detected flar…
Figure 6
Figure 6. Figure 6: Observed binned unabsorbed energy differential distribution of XVP and Post-XVP (black lines) flares (see Tables 2 and 3), with Poisson errors on the number of flares in that bin divided by its width. The colored shaded regions are the associated 70% confidence regions…
Figure 7
Figure 7. Figure 7: Top: 2-8 keV light curve of ObsID 14392 (combined zeroth and first order events) in 300s bins. Shown in red are the Bayesian Blocks. Bottom: Simulated light curve using the same quiescence and flare parameters as those measured in ObsID 14392 as input. The two flares d…
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Number of change points detected in 1000 random Poisson-generated signal-free light curves divided by 1000 (p0) as a function of ncp_prior for different number of expected counts N in the light curves. The noise count rate is 0.01 ct s-1. The red horizontal dotted line…
Figure 10
Figure 10. Figure 10: Summary plot showing the results of 10 different calibration runs for different noise count rates with p0 = 0.05. Each red point represents the mean value of ncp_prior found across those runs for that given expected number of events N, and the error bars are their sta…
Figure 11
Figure 11. Figure 11: Light curve of ObsID 15042. Notice the block separating the two flares. 0 10 20 30 40 50 60 Sorted flare index 10 2 10 3 10 4 Duration (s) 0 10 20 30 40 50 60 Flare index 10 2 10 3 10 4 Duration (s) [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]
Figure 12
Figure 12. Figure 12: Durations of the flares detected in this work (green ’+’), Neilsen et al. (2013) (red triangles), Mossoux & Grosso (2017) (blue ’x’) and Ponti et al. (2015) (black dots). T op: Flares ordered by the shortest duration seen for each flare. Bottom : Flares ordered by the…
Figure 13
Figure 13. Figure 13: Like [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 45 canonical work pages

  1. [1]

    1996, in Astronomical Data Analysis Software and Systems V, Vol

    Arnaud, K. 1996, in Astronomical Data Analysis Software and Systems V, Vol. 101, 17 Baganoff, F., Bautz, M., Brandt, W. N., et al. 2001, Nature, 413, 45 Baganoff, F. K., Maeda, Y., Morris, M., et al. 2003, The Astrophysical Journal, 591, 891

    work page 1996

  2. [2]

    2016, The Astrophysical Journal, 826, 77

    Ball, D., Özel, F., Psaltis, D., & Chan, C.-k. 2016, The Astrophysical Journal, 826, 77

    work page 2016

  3. [3]

    2018, The Astrophysical Journal, 853, 184

    Ball, D., Özel, F., Psaltis, D., Chan, C.-K., & Sironi, L. 2018, The Astrophysical Journal, 853, 184

    work page 2018

  4. [4]

    M., Tomsick, J

    Barriere, N. M., Tomsick, J. A., Baganoff, F. K., et al. 2014, The Astrophysical Journal, 786, 46

    work page 2014

  5. [5]

    2005, The Astrophysical Journal, 635, 1095

    Belanger, G., Goldwurm, A., Melia, F., et al. 2005, The Astrophysical Journal, 635, 1095

    work page 2005

  6. [6]

    2019, The Astrophysical Journal, 871, 161 Čadež, A., Calvani, M., & Kostić, U

    Boyce, H., Haggard, D., Witzel, G., et al. 2019, The Astrophysical Journal, 871, 161 Čadež, A., Calvani, M., & Kostić, U. 2008, Astronomy & Astrophysics, 487, 527 Coti Zelati, F., Rea, N., Turolla, R., et al. 2017, MNRAS, 471, 1819

    work page 2019

  7. [7]

    Davis, M. H. 1984, Journal of the Royal Statistical Society: Series B (Methodological), 46, 353

    work page 1984

  8. [8]

    2013, The Astrophysical Journal, 769, 155

    Degenaar, N., Miller, J., Kennea, J., et al. 2013, The Astrophysical Journal, 769, 155

    work page 2013

Show all 54 references
  1. [9]

    2014, Monthly Notices of the Royal Astronomical Society, 441, 1005 —

    Dibi, S., Markoff, S., Belmont, R., et al. 2014, Monthly Notices of the Royal Astronomical Society, 441, 1005 —. 2016, Monthly Notices of the Royal Astronomical Society, 461, 552

    2014

  2. [10]

    K., et al

    Do, T., Witzel, G., Gautam, A. K., et al. 2019, arXiv preprint arXiv:1908.01777

    2019 arXiv

  3. [11]

    2009, The Astrophysical Journal, 698, 676

    Dodds-Eden, K., Porquet, D., Trap, G., et al. 2009, The Astrophysical Journal, 698, 676

    2009

  4. [12]

    2009, Astronomy & Astrophysics, 500, 935 EHT Collaboration, A

    Eckart, A., Baganoff, F., Morris, M., et al. 2009, Astronomy & Astrophysics, 500, 935 EHT Collaboration, A. K., Alberdi, A., Alef, W., et al. 2019, The Astrophysical Journal, 875, L1

    2009

  5. [13]

    2010, Reviews of Modern Physics, 82, 3121

    Genzel, R., Eisenhauer, F., & Gillessen, S. 2010, Reviews of Modern Physics, 82, 3121

    2010

  6. [14]

    2012, Nature, 481, 51 —

    Gillessen, S., Genzel, R., Fritz, T., et al. 2012, Nature, 481, 51 —. 2013a, The Astrophysical Journal, 763, 78

    2012

  7. [15]

    2017, The Astrophysical Journal, 837, 30

    Gillessen, S., Plewa, P., Eisenhauer, F., et al. 2017, The Astrophysical Journal, 837, 30

    2017

  8. [16]

    2018, arXiv preprint arXiv:1812.01416

    Gillessen, S., Plewa, P., Widmann, F., et al. 2018, arXiv preprint arXiv:1812.01416

    2018 arXiv

  9. [17]

    2003, The Astrophysical Journal, 584, 751 Gravity Collaboration, A

    Goldwurm, A., Brion, E., Goldoni, P., et al. 2003, The Astrophysical Journal, 584, 751 Gravity Collaboration, A. R., Amorim, A., Bauböck, M., et al. 2018, Astronomy & Astrophysics, 618, L10

    2003

  10. [18]

    2019, in preparation

    Haggard, D., et al. 2019, in preparation

    2019

  11. [19]

    2017, Publications of the Astronomical Society of Japan, 69, 43

    Kawashima, T., Matsumoto, Y., & Matsumoto, R. 2017, Publications of the Astronomical Society of Japan, 69, 43

    2017

  12. [20]

    2013, The Astrophysical Journal Letters, 770, L24 Kostić, U., Čadež, A., Calvani, M., & Gomboc, A

    Kennea, J., Burrows, D., Kouveliotou, C., et al. 2013, The Astrophysical Journal Letters, 770, L24 Kostić, U., Čadež, A., Calvani, M., & Gomboc, A. 2009, Astronomy & Astrophysics, 496, 307

    2013

  13. [21]

    2002, The Astrophysical Journal Letters, 566, L77

    Liu, S., & Melia, F. 2002, The Astrophysical Journal Letters, 566, L77

    2002

  14. [22]

    2004, The Astrophysical Journal Letters, 611, L101

    Liu, S., Petrosian, V., & Melia, F. 2004, The Astrophysical Journal Letters, 611, L101

    2004

  15. [23]

    Madigan, A.-M., McCourt, M., & O’Leary, R. M. 2016, Monthly Notices of the Royal Astronomical Society, stw2815 Markoff, S., Falcke, H., Yuan, F., & Biermann, P. L. 2001, Astronomy & Astrophysics, 379, L13

    2016

  16. [24]

    P., Baganoff, F., Morris, M., et al

    Marrone, D. P., Baganoff, F., Morris, M., et al. 2008, The Astrophysical Journal, 682, 373

    2008

  17. [25]

    V., Zhang, S., et al

    Mori, K., Gotthelf, E. V., Zhang, S., et al. 2013, The Astrophysical Journal Letters, 770, L23

    2013

  18. [26]

    2017, Astronomy & Astrophysics, 604, A85

    Mossoux, E., & Grosso, N. 2017, Astronomy & Astrophysics, 604, A85

    2017

  19. [27]

    H., & Porquet, D

    Mossoux, E., Grosso, N., Vincent, F. H., & Porquet, D. 2015, Astronomy & Astrophysics, 573, A46

    2015

  20. [28]

    2016, Astronomy & Astrophysics, 589, A116

    Mossoux, E., Grosso, N., Bushouse, H., et al. 2016, Astronomy & Astrophysics, 589, A116

    2016

  21. [29]

    2013, The Astrophysical Journal, 774, 42

    Neilsen, J., Nowak, M., Gammie, C., et al. 2013, The Astrophysical Journal, 774, 42

    2013

  22. [30]

    2015, The Astrophysical Journal, 799, 199

    Neilsen, J., Markoff, S., Nowak, M., et al. 2015, The Astrophysical Journal, 799, 199

    2015

  23. [31]

    2012, The Astrophysical Journal, 759, 95

    Nowak, M., Neilsen, J., Markoff, S., et al. 2012, The Astrophysical Journal, 759, 95

    2012

  24. [32]

    2015, The Astrophysical Journal, 798, 111

    Pfuhl, O., Gillessen, S., Eisenhauer, F., et al. 2015, The Astrophysical Journal, 798, 111

    2015

  25. [33]

    2015, Monthly Notices of the Royal Astronomical Society, 454, 1525

    Ponti, G., De Marco, B., Morris, M., et al. 2015, Monthly Notices of the Royal Astronomical Society, 454, 1525

    2015

  26. [34]

    2016, Monthly Notices of the Royal Astronomical Society, 461, 2688

    Ponti, G., Jin, C., De Marco, B., et al. 2016, Monthly Notices of the Royal Astronomical Society, 461, 2688

    2016

  27. [35]

    2017, Monthly Notices of the Royal Astronomical Society, 468, 2447

    Ponti, G., George, E., Scaringi, S., et al. 2017, Monthly Notices of the Royal Astronomical Society, 468, 2447

    2017

  28. [36]

    2003, Astronomy & Astrophysics, 407, L17

    Porquet, D., Predehl, P., Aschenbach, B., et al. 2003, Astronomy & Astrophysics, 407, L17

    2003

  29. [37]

    2008, Astronomy & Astrophysics, 488, 549

    Porquet, D., Grosso, N., Predehl, P., et al. 2008, Astronomy & Astrophysics, 488, 549

    2008

  30. [38]

    2002, The Astrophysical Journal, 575, 855 —

    Quataert, E. 2002, The Astrophysical Journal, 575, 855 —. 2003, Astronomische Nachrichten: Astronomical Notes, 324, 435 22

    2002

  31. [39]

    J., & Brunthaler, A

    Reid, M. J., & Brunthaler, A. 2004, The Astrophysical Journal, 616, 872

    2004

  32. [40]

    D., Norris, J

    Scargle, J. D., Norris, J. P., Jackson, B., & Chiang, J. 2013, The Astrophysical Journal, 764, 167

    2013

  33. [41]

    2012, The Astrophysical Journal, 755, 155

    Schartmann, M., Burkert, A., Alig, C., et al. 2012, The Astrophysical Journal, 755, 155

    2012

  34. [42]

    I., & Sunyaev, R

    Shakura, N. I., & Sunyaev, R. A. 1973, Astronomy and Astrophysics, 24, 337

    1973

  35. [43]

    J., Korista, K., & Yakovlev, D

    Verner, D., Ferland, G. J., Korista, K., & Yakovlev, D. 1996, arXiv preprint astro-ph/9601009

    1996 arXiv

  36. [44]

    2013, Science, 341, 981

    Wang, Q., Nowak, M., Markoff, S., et al. 2013, Science, 341, 981

    2013

  37. [45]

    K., Clavel, M., Newton, E., & Ryzhkov, D

    Williams, P. K., Clavel, M., Newton, E., & Ryzhkov, D. 2017, Astrophysics Source Code Library

    2017

  38. [46]

    2000, The Astrophysical Journal, 542, 914

    Wilms, J., Allen, A., & McCray, R. 2000, The Astrophysical Journal, 542, 914

    2000

  39. [47]

    2012, The Astrophysical Journal Supplement Series, 203, 18

    Witzel, G., Eckart, A., Bremer, M., et al. 2012, The Astrophysical Journal Supplement Series, 203, 18

    2012

  40. [48]

    Xu, Y.-D., Narayan, R., Quataert, E., Yuan, F., & Baganoff, F. K. 2006, The Astrophysical Journal, 640, 319

    2006

  41. [49]

    2014, Annual Review of Astronomy and Astrophysics, 52, 529

    Yuan, F., & Narayan, R. 2014, Annual Review of Astronomy and Astrophysics, 52, 529

    2014

  42. [50]

    2003, The Astrophysical Journal, 598, 301

    Yuan, F., Quataert, E., & Narayan, R. 2003, The Astrophysical Journal, 598, 301

    2003

  43. [51]

    Yuan, Q., & Wang, Q. D. 2016, Monthly Notices of the Royal Astronomical Society, 456, 1438

    2016

  44. [52]

    D., Liu, S., & Wu, K

    Yuan, Q., Wang, Q. D., Liu, S., & Wu, K. 2018, Monthly Notices of the Royal Astronomical Society, 473, 306

    2018

  45. [53]

    K., Ponti, G., et al

    Zhang, S., Baganoff, F. K., Ponti, G., et al. 2017, The Astrophysical Journal, 843, 96

    2017

  46. [54]

    2012, Monthly Notices of the Royal Astronomical Society, 421, 1315

    Zubovas, K., Nayakshin, S., & Markoff, S. 2012, Monthly Notices of the Royal Astronomical Society, 421, 1315

    2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.