Pith. sign in

REVIEW 2 major objections 4 minor 78 references

Variable QPO lags and reduced coherence between the disc and corona in MAXI J1820+070

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read This paper establishes that QPO lags between the disc and corona in MAXI J1820+070 vary with coronal flux on timescales of tens of seconds, and that the QPO suppresses disc-corona coherence at and below its frequency.

desk verdict A solid and genuinely new spectral-timing result on MAXI J1820+070, with the flux-binning caveat being real but addressable; deserves peer review and a requested simulation. read the letter →

arxiv 2507.18437 v1 pith:M5XF5VSS submitted 2025-07-24 astro-ph.HE

Niek Bollemeijer , Phil Uttley This is my paper · ORCID
classification astro-ph.HE
keywords quasi-periodicoscillationsX-raytimingcoherencedisc-coronalagsMAXIJ1820+070blackholebinariescoronalgeometryNICER
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 uses NICER observations of the black hole X-ray binary MAXI J1820+070 to test whether the time delay between soft disc photons and harder coronal photons at the quasi-periodic oscillation (QPO) frequency is a fixed property. It finds that the QPO lag between a soft band with significant disc emission and a hard coronal power-law band varies strongly with the instantaneous 3-10 keV flux, changing by more than half a radian on timescales of tens of seconds, and that the variation is largest at QPO frequencies below about 0.3 Hz. The same observations show that coherence between disc and coronal bands drops sharply at and below the QPO frequency, even though it stays high above it. The paper argues this points to a corona whose geometry changes on timescales a few times longer than the QPO cycle, rather than a static reprocessing geometry.

What carries the argument

The central object is the frequency-dependent cross-spectrum between five NICER energy bands. The authors define five bands from 0.3-10 keV, with the softest two dominated by disc emission and the hardest by coronal power-law emission. Using 18-second segments, about three QPO cycles long, they sort the data into four hard-flux bins and then measure phase lags and intrinsic coherence in the third Fourier bin, which sits on the QPO fundamental. The coherence function, which measures whether variability in two bands is linearly related, is the quantity that reveals the QPO-linked drop, and the lag-versus-flux slope in radians per normalised count rate quantifies how much the lag itself changes.

What would settle it

Simulate light curves with constant intrinsic QPO lags and the measured ~10% spectral component changes, apply the same 18-second flux binning and third-bin lag estimate, and check whether a >0.5 rad lag difference between flux bins emerges; if it does, the intrinsic-lag conclusion fails.

Watch

Extended reading notes

Core claim

The central claim is that the QPO hard lag between disc-dominated soft X-rays and coronal power-law X-rays in MAXI J1820+070 is intrinsically variable, anticorrelated with the hard (3-10 keV) flux, and that the QPO mechanism suppresses linear coherence between disc and coronal bands at and below the QPO frequency. Specifically, the lag at the QPO fundamental between 0.3-0.6 keV and 3-10 keV is almost 1 rad in the lowest hard-flux bin and consistent with zero in the highest bin, with a difference exceeding 0.5 rad across bins, while lags between power-law-dominated bands show little or no flux dependence. The paper shows the coherence drop tracks the QPO frequency across the outburst, appears in the high-inclination source MAXI J1803-298 but not in low-inclination GX 339-4, and is absent in an early observation without a QPO. The authors interpret these patterns as evidence that the vertical extent or geometry of the corona changes on timescales slightly longer than the QPO cycle, which would explain both the variable lags and the reduced coherence.

Load-bearing premise

The analysis assumes that grouping 18-second segments by their 3-10 keV count rate and measuring the lag in the QPO bin does not itself create the lag-flux relation through selection effects or through a changing mixture of disc and coronal emission.

Editorial extensions

If this is right

  • Time-averaged QPO lag measurements will systematically underestimate the disc-corona lag whenever the hard flux varies within the averaging window.
  • Any QPO model that assumes fixed geometry and fixed lags on timescales of tens of seconds is inconsistent with the observed lag swings of more than half a radian.
  • The coherence boundary at the QPO frequency implies the QPO mechanism adds or removes variability that is not shared linearly between disc and corona, so unity-coherence assumptions at these frequencies are invalid.
  • The presence of the effect in two high-inclination sources and its absence in a low-inclination source points to a viewing-angle-dependent geometric origin, strongest when the disc is seen more edge-on.
  • During the bright decline, the lag and coherence features persist at the expected QPO frequency even when no power-spectral peak is detectable, so the timing signature can outlive the power-spectrum signature.

Reading between the lines

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

  • A quantitative simulation of the flux-binning selection effect, with constant intrinsic lags and the measured ~10% spectral changes, would settle whether the intrinsic-lag conclusion holds; the paper argues against the mixing explanation but does not run such a simulation.
  • If changing coronal height drives the effect, then phase-resolved spectroscopy within each QPO cycle should show the disc illumination pattern varying with QPO phase, a testable prediction that follows from the geometric interpretation.
  • The steepening of the lag-flux slope below roughly 0.3 Hz predicts that other bright high-inclination hard-state sources with low QPO frequencies should show even larger lag swings, providing a direct observational check.
  • Searching for coherence dips alone, rather than requiring a power-spectral QPO peak, could reveal QPO mechanisms in fainter sources or in states where the QPO is too weak to be detected in the power spectrum.
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

2 major / 4 minor

Summary. The paper presents a spectral-timing analysis of NICER observations of the black hole X-ray binary MAXI J1820+070 in its 2018 hard state, focusing on type-C quasi-periodic oscillations (QPOs). The authors measure cross-spectral phase lags and coherence between soft energy bands (with significant disc emission) and a harder 3-10 keV band, and further study how the QPO lags depend on the instantaneous 3-10 keV flux by binning 18-s light-curve segments into four flux bins. They report that the QPO hard lag between disc-dominated and power-law-dominated bands anticorrelates with the hard flux, varying on timescales of a few QPO cycles, with the effect strongest below about 0.3 Hz. They also find that the intrinsic coherence between disc and power-law bands is strongly reduced at and below the QPO frequency, recovering above it, and that this pattern tracks the QPO frequency across the outburst. The same coherence and lag features are observed in the high-inclination source MAXI J1803-298 but not in the lower-inclination source GX 339-4. The authors interpret these results as evidence for variations in the vertical extent of the corona on timescales slightly longer than the QPO cycle.

Significance. If the results hold, this is a significant observational contribution to the study of QPOs and disc-corona coupling. The paper provides a direct, high-significance measurement that QPO lags are not stationary but vary on timescales of tens of seconds, and it reports a previously unrecognized link between the QPO frequency and a coherence drop at and below that frequency. It also offers a promising inclination dependence, with the effect present in two high-inclination sources and absent in one lower-inclination source, which is a falsifiable prediction for QPO models. The analysis uses standard cross-spectral methods, reports significance values above 5 sigma for the softest bands in multiple observation groups, and includes checks such as spectral fitting of flux bins and a comparison to a pre-QPO observation. The paper promises a reproduction package on Zenodo, which aids reproducibility. The main caveat, discussed below, is that the flux-binning analysis used for the variable-lag claim needs an explicit quantitative demonstration that selection effects and spectral mixing cannot produce the observed lag-flux relation.

major comments (2)
  1. [Section 4.1 and Fig. 9] The conclusion that the QPO lags are intrinsically variable rests on ruling out the possibility that the observed lag-flux relation is produced by flux-dependent changes in the spectral composition of the soft band. The authors show that the diskbb/thcomp ratio changes by only about 10% between flux bins, but they do not translate this change into a predicted phase-lag difference. For a two-component mixture in the soft band (S = αD + βP) measured against a power-law-dominated reference (P), the measured cross-spectral phase is arg(αg e^{iφ} + β), where g is the disc-to-power-law variability amplitude ratio at the QPO frequency and φ is the intrinsic disc-power-law phase lag. If φ ≈ π/2 and g ≈ 1, a 10% change in α/β changes this phase by only about 0.05 rad, far less than the observed >0.5 rad difference. The authors' qualitative conclusion is therefore likely correct, but the paper should include this calculation or a small simulation, and should propagate the spectral-fit uncertainties into the predicted lag range, rather than leaving the inference implicit.
  2. [Section 3.1 and Fig. 1] The flux-binning analysis uses the 3-10 keV count rate both as the variable that defines the flux bins and as the reference band for the cross-spectral phase measurements. Because of the rms-flux relation, the flux bins differ systematically in QPO amplitude and signal-to-noise, and the paper does not assess whether this selection can induce a spurious lag-flux correlation when the true QPO phase is constant. This is a load-bearing point for the claim that the QPO lags change intrinsically. The authors should test this explicitly, for example by simulating a stationary process with a constant QPO phase and an rms-flux relation, applying the same 18-s segment binning and phase estimation, and showing that the recovered lag-flux slope is consistent with zero at the level of the observed slopes. Without such a test, part of the reported lag-flux signal could in principle be an artifact of the binning procedure.
minor comments (4)
  1. [Section 2, Table 2] In the first paragraph of Section 2, the text states that the five energy bands are 'listed in Table 1', but the energy bands are actually listed in Table 2; this cross-reference should be corrected.
  2. [Section 3.1, Fig. 1 caption] The caption states that the y-axis is scaled logarithmically, but the same panel shows phase lags that can be near zero or negative; please clarify how negative or zero values are displayed, or use a linear or symlog axis.
  3. [Section 3.1, after Eq. (1)] The text says that the reported σ values represent the probability of obtaining the data if there were no relation; these are significance levels in Gaussian sigmas, not probabilities, and the wording should be adjusted to avoid confusion.
  4. [Section 4.4, ObsID 187] The term 'imaginary QPO' is introduced through a reference to Bellavita et al. (2025) but is not defined in the present paper; a brief definition or explanation of the cross-spectral feature would help the reader.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the QPO lag and coherence measurements are direct observational products, and the self-citations are motivational rather than load-bearing.

full rationale

The paper's central results are derived from NICER light curves through standard Fourier cross-spectral methods: QPO frequencies are fitted from power spectra, segments are binned by 3-10 keV flux, and lags and coherence are measured in a Fourier bin dominated by the QPO. None of these steps defines the output in terms of the input. The lag-flux relation is an observed correlation, not a quantity fitted from the same data as a 'prediction.' The coherence drop at and below the QPO frequency is also a direct measurement and is shown to track the QPO frequency from observation to observation. The spectral fits in Section 4.1 are used to test whether a changing disc/power-law mixture could explain the lag changes; even if that test is not fully quantitative, it is an attempt to rule out an alternative interpretation, not a circular reduction. The authors cite their own prior work (Bollemeijer et al. 2024) as motivation and context, but the present analysis is described in full and does not depend on any unverified self-citation. No uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The skeptic's concern about selection biases and two-component mixing is a legitimate correctness/robustness worry, but it is not an instance of the paper's derivation being equivalent to its inputs by construction. Therefore the appropriate circularity score is 0.

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

The central lag and coherence measurements do not depend on fitted model parameters. The only fitted model, tbabs*(diskbb+thcomp*bbody), is used as a supporting check in Section 4.1 to estimate disc/corona mixing; its parameters are not used to define the central result. The main load-bearing assumptions are about the validity of the Fourier estimators, the purity of the energy bands, and the absence of strong selection effects in the flux-binned analysis.

assumptions (5)
  • standard math Fourier cross-spectral estimators (phase lags, intrinsic coherence) with Poisson noise corrections are valid for the NICER light curves.
    Section 3.1 and Figures 1, 5-8; the methods follow Vaughan & Nowak (1997) and Uttley et al. (2014), which are established in the field.
  • domain assumption The third Fourier bin of 18 s segments captures the QPO signal well enough that the measured phase in that bin can be interpreted as the QPO lag.
    Section 3.1 defines QPO lags as the phase in the third Fourier frequency bin for segments of about three QPO cycles; this assumes the QPO peak dominates that bin and that leakage from broadband noise is small.
  • domain assumption The 3-10 keV band is dominated by coronal power-law emission and can serve as a reference for disc-corona comparisons.
    Table 2 labels 3-10 keV as the hard power-law band; the interpretation of lag and coherence measurements assumes this band is not strongly contaminated by disc or reflection emission.
  • domain assumption The spectral model tbabs*(diskbb+thcomp*bbody) captures the broad-band spectrum well enough to estimate disc/corona mixing ratios.
    Section 4.1 and Figure 9 add a 5% systematic error and ignore reflection and calibration features; the conclusion that the component ratio changes by only about 10% rests on this model being adequate.
  • domain assumption There is no strong long-term count-rate trend within the combined observations, so segment-level flux binning is meaningful.
    Section 3.1, panel D of Figure 1, where the authors check that no dominant long-term trend exists before binning.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Variable QPO lags and reduced coherence between the disc and corona in MAXI J1820+070." pith.science (2026). https://pith.science/paper/M5XF5VSS

@misc{pith2026250718437,
  author       = {Pith},
  title        = {Pith review of: Variable QPO lags and reduced coherence between the disc and corona in MAXI J1820+070},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M5XF5VSS}},
  note         = {Machine review of arXiv:2507.18437}
}
abstract

Quasi-periodic oscillations (QPOs) are observed in the hard state of many black hole X-ray binaries. Although their origin is unknown, they are strongly associated with the corona, of which the geometry is also subject to discussion. We present a thorough spectral-timing analysis of QPOs and broadband noise in the high-inclination BHXRB MAXI J1820+070, using the rich NICER data set of the source in the bright hard state of its outburst in 2018. We find that there is a large QPO hard lag between soft energy bands with significant disc emission and harder coronal power-law bands, which is absent when measuring lags between energy bands dominated by the coronal emission. The QPO lags between a soft band (with significant disc emission) and harder coronal power-law bands vary significantly with power-law flux, on time-scales of (tens of) seconds or a few QPO cycles, especially at QPO frequencies $\lesssim0.3$ Hz. At the same time, the QPO is found to be related to a decreased coherence between energy bands with significant disc emission and harder bands both at and below the QPO frequency, suggesting the QPO mechanism filters out part of the variability. Similar patterns in the frequency-dependent lags and coherence are observed in the BHXRB MAXI J1803-298, which is a (dipping) high-inclination source, but not in the low-inclination source GX 339-4. We suggest that these findings may be evidence of changes in the vertical extent of the corona on time-scales slightly longer than the QPO cycle.

Figures

Figures reproduced from arXiv: 2507.18437 by the authors.

Figure 1
Figure 1. The five panels shown in the figure summarise the methods and results for finding the relation between the 3-10 keV flux and the QPO lags in observations 135, 136 and 137, which all have a QPO fundamental frequency of ∼0.17 Hz, indicated with the grey shaded area. In the left column, we show the power-spectra, phase lag- and coherence versus frequency spectra for four hard flux bins, using the very soft 0.3-0.6 and … view at source ↗
Figure 2
Figure 2. QPO lag- and coherence energy spectra for ObsIDs 135, 136 and 137 are shown for four hard flux bins in the top and bottom panels. The hard band is always 3-10 keV. It is clear that the lags start to deviate with flux below 1.3 keV, where disc emission is important. We follow the convention that hard lags move in the positive direction (upwards) with increasing energy, so the negative values at energies below 3 keV r… view at source ↗
Figure 3
Figure 3. The upper panels show lag-frequency spectra between the very soft and hard band for three groups of observations with different QPO frequencies in four hard (3-10 keV) flux bins. It is clear that the lags at (and below) the QPO frequency depend strongly on the hard flux, with a clear peak of almost 1 rad at the QPO frequency for the lowest flux bin, while there is no feature for the highest flux bin. The lower panel… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The relation between the slope of the QPO lag – flux relation versus the QPO frequency for all observations in the hard state and HIMS of MAXI J1820+070 (ObsIDs 106-196). Almost all slopes are negative (harder lags for low hard flux) and for the very soft band the rela…
Figure 5
Figure 5. Figure 5: Power spectra, phase lag and coherence versus frequency spectra for different combinations of energy bands of observations 104 (left), 108 (middle) and 145 (right). The lags and coherence all have 3-10 keV as their hard band, while the different colours are defined by …
Figure 6
Figure 6. Figure 6: The coherence versus frequency spectra for the very soft (0.3-0.6 keV) and hard (3-10 keV) band for five observations with increasing QPO frequency. The grey shaded areas show the QPO centroid frequency ± HWHM. It is clear the QPO frequency marks the border of the low …
Figure 7
Figure 7. Figure 7: The power spectra and phase lag and coherence versus frequency spectra for a subset of data from observation 4202130104 of MAXI J1803- 298. At the QPO frequency, around 0.27 Hz, a clear increase in the phase lags between the soft bands below 1.3 and the 3-10 keV hard b…
Figure 9
Figure 9. Figure 9: The upper panel shows the spectra of four hard flux bins of ObsIDs 135, 136 and 137, fitted with a tbabs*(diskbb+thcomp*bbody) model. The covering fraction of the thcomp component is set to 1, so all emission is from Comptonization, and the temperature of bbody is equa…
Figure 10
Figure 10. Figure 10: The power spectra, phase lag and coherence versus frequency spectra for MAXI J1820+070 observation 1200120187 during the bright decline of the outburst. The luminosity is a factor of three lower than during the peak of the hard state, and we observe no significant QPO…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

78 extracted references · 6 canonical work pages

  1. [1]

    K., et al., 2024, @doi [ ] 10.3847/1538-4357/ad82e9 , https://ui.adsabs.harvard.edu/abs/2024ApJ...977...26A 977, 26

    Adegoke O. K., et al., 2024, @doi [ ] 10.3847/1538-4357/ad82e9 , https://ui.adsabs.harvard.edu/abs/2024ApJ...977...26A 977, 26

    doi:10.3847/1538-4357/ad82e9 2024

  2. [3]

    Bambi C., et al., 2021, @doi [ ] 10.1007/s11214-021-00841-8 , https://ui.adsabs.harvard.edu/abs/2021SSRv..217...65B 217, 65

    doi:10.1007/s11214-021-00841-8 2021

  3. [4]

    Bellavita C., Garc \' a F., M \'e ndez M., Karpouzas K., 2022, @doi [ ] 10.1093/mnras/stac1922 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.2099B 515, 2099

    doi:10.1093/mnras/stac1922 2022

  4. [5]

    Bellavita C., M \'e ndez M., Garc \' a F., Ma R., K \"o nig O., 2025, @doi [ ] 10.1051/0004-6361/202453092 , https://ui.adsabs.harvard.edu/abs/2025A&A...696A.128B 696, A128

    doi:10.1051/0004-6361/202453092 2025

  5. [6]

    Bollemeijer N., et al., 2024, @doi [ ] 10.1093/mnras/stad3912 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528..558B 528, 558

    doi:10.1093/mnras/stad3912 2024

  6. [7]

    arXiv:2505.07938

    Brigitte M., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2505.07938 , https://ui.adsabs.harvard.edu/abs/2025arXiv250507938B p. arXiv:2505.07938

    doi:10.48550/arxiv.2505.07938 2025

  7. [8]

    Buisson D. J. K., et al., 2019, @doi [ ] 10.1093/mnras/stz2681 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1350B 490, 1350

    doi:10.1093/mnras/stz2681 2019

  8. [9]

    Buisson D. J. K., Marcel G., López-Barquero V., Motta S. E., Turner S. G. D., Vincentelli F. M., 2025 ( @eprint arXiv 2502.08718 )

    arXiv 2025

Show all 78 references
  1. [10]

    Casella P., Belloni T., Stella L., 2005, @doi [ ] 10.1086/431174 , https://ui.adsabs.harvard.edu/abs/2005ApJ...629..403C 629, 403

    2005 doi

  2. [13]

    M., Casares J., Mu \ n oz-Darias T., Bauer F

    Corral-Santana J. M., Casares J., Mu \ n oz-Darias T., Bauer F. E., Mart \' nez-Pais I. G., Russell D. M., 2016, @doi [ ] 10.1051/0004-6361/201527130 , https://ui.adsabs.harvard.edu/abs/2016A&A...587A..61C 587, A61

    2016 doi

  3. [14]

    L., Fabian A

    Dauser T., Garcia J., Parker M. L., Fabian A. C., Wilms J., 2014, @doi [ ] 10.1093/mnrasl/slu125 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444L.100D 444, L100

    2014 doi

  4. [15]

    A., Ponti G., Migliori G., Belloni T

    De Marco B., Zdziarski A. A., Ponti G., Migliori G., Belloni T. M., Segovia Otero A., Dzie ak M. A., Lai E. V., 2021, @doi [ ] 10.1051/0004-6361/202140567 , https://ui.adsabs.harvard.edu/abs/2021A&A...654A..14D 654, A14

    2021 doi

  5. [16]

    De Ruiter I., van den Eijnden J., Ingram A., Uttley P., 2019, @doi [ ] 10.1093/mnras/stz665 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.3834D 485, 3834

    2019 doi

  6. [17]

    Fan N., et al., 2024, @doi [ ] 10.3847/1538-4357/ad49a1 , https://ui.adsabs.harvard.edu/abs/2024ApJ...969...61F 969, 61

    2024 doi

  7. [18]

    O., Henri G., Saug \'e L., Pelletier G., 2006, @doi [ ] 10.1051/0004-6361:20052689 , https://ui.adsabs.harvard.edu/abs/2006A&A...447..813F 447, 813

    Ferreira J., Petrucci P. O., Henri G., Saug \'e L., Pelletier G., 2006, @doi [ ] 10.1051/0004-6361:20052689 , https://ui.adsabs.harvard.edu/abs/2006A&A...447..813F 447, 813

    2006 doi

  8. [19]

    Ferreira J., et al., 2022, @doi [ ] 10.1051/0004-6361/202040165 , https://ui.adsabs.harvard.edu/abs/2022A&A...660A..66F 660, A66

    2022 doi

  9. [20]

    A., Garc \' a F., M \'e ndez M., K \"o nig O., Wilms J., 2025, @doi [ ] 10.1051/0004-6361/202453523 , https://ui.adsabs.harvard.edu/abs/2025A&A...696A.237F 696, A237

    Fogantini F. A., Garc \' a F., M \'e ndez M., K \"o nig O., Wilms J., 2025, @doi [ ] 10.1051/0004-6361/202453523 , https://ui.adsabs.harvard.edu/abs/2025A&A...696A.237F 696, A237

    2025 doi

  10. [21]

    C., Blaes O

    Fragile P. C., Blaes O. M., Anninos P., Salmonson J. D., 2007, @doi [ ] 10.1086/521092 , https://ui.adsabs.harvard.edu/abs/2007ApJ...668..417F 668, 417

    2007 doi

  11. [22]

    Garc \' a J., et al., 2014, @doi [ ] 10.1088/0004-637X/782/2/76 , https://ui.adsabs.harvard.edu/abs/2014ApJ...782...76G 782, 76

    2014 doi

  12. [23]

    A., Steiner J

    Garc \' a J. A., Steiner J. F., McClintock J. E., Remillard R. A., Grinberg V., Dauser T., 2015, @doi [ ] 10.1088/0004-637X/813/2/84 , https://ui.adsabs.harvard.edu/abs/2015ApJ...813...84G 813, 84

    2015 doi

  13. [24]

    García F., Karpouzas K., Méndez M., Zhang L., Zhang Y., Belloni T., Altamirano D., 2022, @doi [ ] 10.1093/mnras/stac1202 , 513, 4196

    2022 doi

  14. [25]

    HEASARC 2014, HEAsoft: Unified Release of FTOOLS and XANADU , Astrophysics Source Code Library, record ascl:1408.004

    2014

  15. [26]

    M., Vaughan S., Uttley P., 2011, @doi [ ] 10.1111/j.1745-3933.2010.00997.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.411L..66H 411, L66

    Heil L. M., Vaughan S., Uttley P., 2011, @doi [ ] 10.1111/j.1745-3933.2010.00997.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.411L..66H 411, L66

    2011

  16. [27]

    M., Vaughan S., Uttley P., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20824.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.422.2620H 422, 2620

    Heil L. M., Vaughan S., Uttley P., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20824.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.422.2620H 422, 2620

    2012

  17. [28]

    M., Uttley P., Klein-Wolt M., 2015, @doi [ ] 10.1093/mnras/stv191 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.3339H 448, 3339

    Heil L. M., Uttley P., Klein-Wolt M., 2015, @doi [ ] 10.1093/mnras/stv191 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.3339H 448, 3339

    2015 doi

  18. [29]

    Homan J., et al., 2020, @doi [ ] 10.3847/2041-8213/ab7932 , https://ui.adsabs.harvard.edu/abs/2020ApJ...891L..29H 891, L29

    2020 doi

  19. [30]

    Huppenkothen D., Bachetti M., 2018, @doi [ ] 10.3847/1538-4365/aabe38 , 236, 13

    2018 doi

  20. [31]

    Ingram A., 2019, @doi [ ] 10.1093/mnras/stz2409 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.3927I 489, 3927

    2019 doi

  21. [32]

    Ingram A., Done C., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18860.x , 415, 2323

    2011

  22. [33]

    R., Motta S

    Ingram A. R., Motta S. E., 2019, @doi [New Astronomy Reviews] https://doi.org/10.1016/j.newar.2020.101524 , 85, 101524

    2019

  23. [34]

    C., 2009, @doi [ ] 10.1111/j.1745-3933.2009.00693.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.397L.101I 397, L101

    Ingram A., Done C., Fragile P. C., 2009, @doi [ ] 10.1111/j.1745-3933.2009.00693.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.397L.101I 397, L101

    2009

  24. [35]

    Ingram A., van der Klis M., Middleton M., Done C., Altamirano D., Heil L., Uttley P., Axelsson M., 2016, @doi [ ] 10.1093/mnras/stw1245 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461.1967I 461, 1967

    2016 doi

  25. [36]

    A., 2019, @doi [ ] 10.1093/mnras/stz1720 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488..324I 488, 324

    Ingram A., Mastroserio G., Dauser T., Hovenkamp P., van der Klis M., Garc \' a J. A., 2019, @doi [ ] 10.1093/mnras/stz1720 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488..324I 488, 324

    2019 doi

  26. [37]

    K., Chhotaray B., Kumari N., Gupta S., 2022, @doi [ ] 10.1093/mnras/stac315 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.3922J 511, 3922

    Jana A., Naik S., Jaisawal G. K., Chhotaray B., Kumari N., Gupta S., 2022, @doi [ ] 10.1093/mnras/stac315 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.3922J 511, 3922

    2022 doi

  27. [38]

    A., 2022, Black Holes: Timing and Spectral Properties and Evolution

    Kalemci E., Kara E., Tomsick J. A., 2022, Black Holes: Timing and Spectral Properties and Evolution. Springer Nature Singapore, Singapore, pp 1--43, @doi 10.1007/978-981-16-4544-0_100-1 , https://doi.org/10.1007/978-981-16-4544-0_100-1

    2022 doi

  28. [39]

    Kara E., et al., 2019, @doi [ ] 10.1038/s41586-018-0803-x , https://ui.adsabs.harvard.edu/abs/2019Natur.565..198K 565, 198

    2019 doi

  29. [40]

    Kawamura T., Axelsson M., Done C., Takahashi T., 2022, @doi [ ] 10.1093/mnras/stac045 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..536K 511, 536

    2022 doi

  30. [41]

    Kazanas D., Hua X.-M., Titarchuk L., 1997, @doi [ ] 10.1086/303991 , 480, 735

    1997 doi

  31. [42]

    K \"o nig O., et al., 2024, @doi [ ] 10.1051/0004-6361/202449333 , https://ui.adsabs.harvard.edu/abs/2024A&A...687A.284K 687, A284

    2024 doi

  32. [44]

    D., Papadakis I

    Kylafis N. D., Papadakis I. E., Reig P., Giannios D., Pooley G. G., 2008, @doi [ ] 10.1051/0004-6361:20079159 , https://ui.adsabs.harvard.edu/abs/2008A&A...489..481K 489, 481

    2008 doi

  33. [45]

    E., 1997, @doi [ ] 10.1093/mnras/292.3.679 , 292, 679

    Lyubarskii Y. E., 1997, @doi [ ] 10.1093/mnras/292.3.679 , 292, 679

    1997 doi

  34. [46]

    Ma X., et al., 2021, @doi [Nature Astronomy] 10.1038/s41550-020-1192-2 , https://ui.adsabs.harvard.edu/abs/2021NatAs...5...94M 5, 94

    2021 doi

  35. [47]

    Ma R., M \'e ndez M., Garc \' a F., Sai N., Zhang L., Zhang Y., 2023, @doi [ ] 10.1093/mnras/stad2284 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525..854M 525, 854

    2023 doi

  36. [48]

    D., Done C., 2018, @doi [ ] 10.1093/mnras/stx2359 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2084M 473, 2084

    Mahmoud R. D., Done C., 2018, @doi [ ] 10.1093/mnras/stx2359 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2084M 473, 2084

    2018 doi

  37. [49]

    Marcel G., Neilsen J., 2021, @doi [ ] 10.3847/1538-4357/abcbf9 , https://ui.adsabs.harvard.edu/abs/2021ApJ...906..106M 906, 106

    2021 doi

  38. [50]

    A., Wilms J., 2005, @doi [ ] 10.1086/497628 , https://ui.adsabs.harvard.edu/abs/2005ApJ...635.1203M 635, 1203

    Markoff S., Nowak M. A., Wilms J., 2005, @doi [ ] 10.1086/497628 , https://ui.adsabs.harvard.edu/abs/2005ApJ...635.1203M 635, 1203

    2005 doi

  39. [51]

    Mastroserio G., et al., 2021, @doi [ ] 10.1093/mnras/stab2056 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507...55M 507, 55

    2021 doi

  40. [52]

    M \'e ndez M., Peirano V., Garc \' a F., Belloni T., Altamirano D., Alabarta K., 2024, @doi [ ] 10.1093/mnras/stad3786 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9405M 527, 9405

    2024 doi

  41. [53]

    E., 2016, @doi [Astronomische Nachrichten] 10.1002/asna.201612320 , https://ui.adsabs.harvard.edu/abs/2016AN....337..398M 337, 398

    Motta S. E., 2016, @doi [Astronomische Nachrichten] 10.1002/asna.201612320 , https://ui.adsabs.harvard.edu/abs/2016AN....337..398M 337, 398

    2016 doi

  42. [54]

    Motta S. E., Casella P., Henze M., Mu \ n oz-Darias T., Sanna A., Fender R., Belloni T., 2015, @doi [ ] 10.1093/mnras/stu2579 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.2059M 447, 2059

    2015 doi

  43. [55]

    A., Ingram A., van der Klis M., 2017, @doi [ ] 10.1093/mnras/stx2872 , 474, 2259

    Mushtukov A. A., Ingram A., van der Klis M., 2017, @doi [ ] 10.1093/mnras/stx2872 , 474, 2259

    2017 doi

  44. [56]

    A., Vaughan B

    Nowak M. A., Vaughan B. A., Wilms J., Dove J. B., Begelman M. C., 1999, @doi [ ] 10.1086/306610 , https://ui.adsabs.harvard.edu/abs/1999ApJ...510..874N 510, 874

    1999 doi

  45. [57]

    D., 2021, @doi [ ] 10.1051/0004-6361/202039903 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.112R 646, A112

    Reig P., Kylafis N. D., 2021, @doi [ ] 10.1051/0004-6361/202039903 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.112R 646, A112

    2021 doi

  46. [58]

    Reig P., Kylafis N., Giannios D., 2003, @doi [A&A] 10.1051/0004-6361:20030449 , 403, L15

    2003 doi

  47. [59]

    A., McClintock J

    Remillard R. A., McClintock J. E., 2006, @doi [ ] 10.1146/annurev.astro.44.051905.092532 , 44, 49–92

    2006 arXiv

  48. [60]

    A., \.Z ycki P

    Sobolewska M. A., \.Z ycki P. T., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10489.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.370..405S 370, 405

    2006

  49. [61]

    L., Uttley P., 2016, @doi [ ] 10.1093/mnras/stw1093 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.460.2796S 460, 2796

    Stevens A. L., Uttley P., 2016, @doi [ ] 10.1093/mnras/stw1093 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.460.2796S 460, 2796

    2016 doi

  50. [62]

    Stiele H., Kong A. K. H., 2020, @doi [ ] 10.3847/1538-4357/ab64ef , https://ui.adsabs.harvard.edu/abs/2020ApJ...889..142S 889, 142

    2020 doi

  51. [63]

    Torres M. A. P., Casares J., Jim \'e nez-Ibarra F., Mu \ n oz-Darias T., Armas Padilla M., Jonker P. G., Heida M., 2019, @doi [ ] 10.3847/2041-8213/ab39df , https://ui.adsabs.harvard.edu/abs/2019ApJ...882L..21T 882, L21

    2019 doi

  52. [64]

    Torres M. A. P., Casares J., Jim \'e nez-Ibarra F., \'A lvarez-Hern \'a ndez A., Mu \ n oz-Darias T., Armas Padilla M., Jonker P. G., Heida M., 2020, @doi [ ] 10.3847/2041-8213/ab863a , https://ui.adsabs.harvard.edu/abs/2020ApJ...893L..37T 893, L37

    2020 doi

  53. [65]

    Uttley P., Malzac J., 2025, @doi [ ] 10.1093/mnras/stae2514 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.3284U 536, 3284

    2025 doi

  54. [66]

    M., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04496.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.323L..26U 323, L26

    Uttley P., McHardy I. M., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04496.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.323L..26U 323, L26

    2001

  55. [67]

    M., Vaughan S., 2005, @doi [ ] 10.1111/j.1365-2966.2005.08886.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.359..345U 359, 345

    Uttley P., McHardy I. M., Vaughan S., 2005, @doi [ ] 10.1111/j.1365-2966.2005.08886.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.359..345U 359, 345

    2005

  56. [68]

    Uttley P., Wilkinson T., Cassatella P., Wilms J., Pottschmidt K., Hanke M., B \"o ck M., 2011, @doi [ ] 10.1111/j.1745-3933.2011.01056.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.414L..60U 414, L60

    2011

  57. [69]

    M., Fabian A

    Uttley P., Cackett E. M., Fabian A. C., Kara E., Wilkins D. R., 2014, @doi [ ] 10.1007/s00159-014-0072-0 , https://ui.adsabs.harvard.edu/abs/2014A&ARv..22...72U 22, 72

    2014 doi

  58. [70]

    Uttley P., et al., 2018, The Astronomer's Telegram, https://ui.adsabs.harvard.edu/abs/2018ATel11423....1U 11423, 1

    2018

  59. [71]

    E., Belloni T

    Van den Eijnden J., Ingram A., Uttley P., Motta S. E., Belloni T. M., Gardenier D. W., 2017, @doi [ ] 10.1093/mnras/stw2634 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.464.2643V 464, 2643

    2017 doi

  60. [72]

    Varnière P., Rodriguez J., Tagger M., 2002, @doi [A&A] 10.1051/0004-6361:20020401 , 387, 497

    2002 doi

  61. [73]

    A., Nowak M

    Vaughan B. A., Nowak M. A., 1997, @doi [ ] 10.1086/310430 , https://ui.adsabs.harvard.edu/abs/1997ApJ...474L..43V 474, L43

    1997 doi

  62. [74]

    Veledina A., Poutanen J., Vurm I., 2013, @doi [ ] 10.1093/mnras/stt124 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430.3196V 430, 3196

    2013 doi

  63. [75]

    Wang J., et al., 2020, @doi [ ] 10.3847/1538-4357/ab9ec3 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...44W 899, 44

    2020 doi

  64. [76]

    Wang J., et al., 2021, @doi [ ] 10.3847/2041-8213/abec79 , https://ui.adsabs.harvard.edu/abs/2021ApJ...910L...3W 910, L3

    2021 doi

  65. [77]

    Wang J., et al., 2022, @doi [ ] 10.3847/1538-4357/ac6262 , https://ui.adsabs.harvard.edu/abs/2022ApJ...930...18W 930, 18

    2022 doi

  66. [78]

    T., Ingram A., Bursa M., Wang W., 2020, @doi [ ] 10.3847/1538-4357/ab9838 , https://ui.adsabs.harvard.edu/abs/2020ApJ...897...27Y 897, 27

    You B., \.Z ycki P. T., Ingram A., Bursa M., Wang W., 2020, @doi [ ] 10.3847/1538-4357/ab9838 , https://ui.adsabs.harvard.edu/abs/2020ApJ...897...27Y 897, 27

    2020 doi

  67. [79]

    You B., et al., 2021, @doi [Nature Communications] 10.1038/s41467-021-21169-5 , https://ui.adsabs.harvard.edu/abs/2021NatCo..12.1025Y 12, 1025

    2021 doi

  68. [80]

    A., Zi \'o kowski J., Miko ajewska J., 2019, @doi [ ] 10.1093/mnras/stz1787 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.1026Z 488, 1026

    Zdziarski A. A., Zi \'o kowski J., Miko ajewska J., 2019, @doi [ ] 10.1093/mnras/stz1787 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.1026Z 488, 1026

    2019 doi

  69. [81]

    A., Szanecki M., Poutanen J., Gierli \'n ski M., Biernacki P., 2020, @doi [ ] 10.1093/mnras/staa159 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5234Z 492, 5234

    Zdziarski A. A., Szanecki M., Poutanen J., Gierli \'n ski M., Biernacki P., 2020, @doi [ ] 10.1093/mnras/staa159 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5234Z 492, 5234

    2020 doi

  70. [82]

    Zhang Y., et al., 2022, @doi [ ] 10.1093/mnras/stac690 , 512, 2686

    2022 doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.