Pith. sign in

REVIEW 4 major objections 5 minor 45 references

Accretion disc winds imprint distinct signatures in the optical variability spectrum of black hole transients

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

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

Pith's one-line read Accretion-disc winds leave an inverted fingerprint in the rms spectra of black hole transients.

desk verdict First optical rms spectra of an LMXB: a genuinely new observable, but the headline inverted P-Cygni claim needs proper error analysis before it can carry the diagnostic weight the paper wants. read the letter →

arxiv 2501.04087 v1 pith:KTIZ3AEQ submitted 2025-01-07 astro-ph.HE

F. M. Vincentelli , T. Muñoz Darias This is my paper · ORCID
classification astro-ph.HE
keywords accretiondiscsblackholetransientsX-raybinarieswindsandoutflowsP-CygniprofilesrmsspectroscopyopticalvariabilityV404Cygni
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 establishes that the optical variability spectrum—the root-mean-square (rms) amplitude as a function of wavelength—carries a distinct signature of accretion-disc winds in black hole transients. Using high-cadence spectra of V404 Cygni during its 2015 outburst, the authors compute the first optical rms spectra of a low-mass X-ray binary and find that a P-Cygni profile in the flux spectrum appears inverted in the rms spectrum: variability is enhanced in the blue-shifted absorption part and suppressed in the red-shifted emission part. Emission lines in flux generally appear as absorption in rms, while high-ionisation lines such as He ii behave oppositely, varying more than the continuum. The result matters because it turns rms spectroscopy into a sensitive wind diagnostic, capable of revealing wind-related absorption even when the time-averaged flux spectrum shows no clear P-Cygni profile.

What carries the argument

The central object is the fractional rms spectrum, computed per wavelength element as $\mathrm{rms}_{\mathrm{frac}} = \sqrt{(S^2 - \sigma^2_{\mathrm{err}})/\bar{x}^2}$, where the variance is measured from the time series, the mean square is subtracted, and the error term removes the Poisson-noise contribution. It is evaluated over Fourier frequencies of roughly $2\times10^{-2}$ Hz down to $1\times10^{-4}$ Hz using 37 to 86 spectra per night. The load-bearing mechanism is the contrast in variability between the blue-shifted absorption and red-shifted emission of a P-Cygni profile: the absorption varies more than the continuum while the emission varies less, producing the inverted rms signature that the paper uses as a wind diagnostic.

What would settle it

A control test would compute rms spectra from a comparable black hole transient in an outburst state where no wind is expected: if inverted profiles or line-position dips still appear, they are calibration artefacts rather than wind signatures. Likewise, if simultaneous high-resolution X-ray or UV wind diagnostics showed no correlation between the optical inverted-profile amplitude and the wind column or ionisation state, the interpretation would fail.

Watch

Extended reading notes

Core claim

The paper's central claim is that the presence of a P-Cygni line profile in the flux spectrum leaves a distinct imprint on the rms spectrum: variability is enhanced in the blue part and decreases below the continuum level in the red part of the profile, so the rms profile appears as an inverted version of the flux profile. This is seen most clearly in He i-5876 on days 2 and 6 of the V404 Cygni campaign and in a weaker, asymmetric form in H-alpha on days 7 and 8, where no clear P-Cygni profile is present in flux. The authors interpret the inverted profile as the blue-shifted wind absorption varying more than the adjacent continuum while the redshifted emission varies less, and they show that the feature strengthens during segments with large flux changes. This is consistent with the wind's visibility being regulated by rapid ionisation changes, and it parallels rms signatures associated with ultra-fast outflows in active galactic nuclei.

Load-bearing premise

The interpretation assumes that the measured brightness changes are truly from the source and not from small, wavelength-dependent errors in the calibration that happen to vary along with the flares; if such errors were present, they could create the same dips and peaks at line positions.

Editorial extensions

If this is right

  • Rms spectroscopy becomes a practical wind-search tool for accreting compact objects, complementary to flux spectroscopy.
  • Weak winds that only slightly disturb the flux profile can still be detected through the blue/red asymmetry they create in the rms spectrum, as seen in H-alpha on days 7 and 8.
  • Combining flux and rms spectra strengthens wind detections and helps distinguish source-related absorption from interstellar bands, telluric lines, and reduction artefacts, which do not imprint on the rms spectrum.
  • The technique extends naturally to other accreting systems, including accreting white dwarfs, where large samples of optically bright objects are available, and to AGN where ionisation-driven rms variability has already been reported.

Reading between the lines

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

  • If the inverted profile is produced by ionisation-driven variability of the wind, then time-lagging the blue and red components across a flare could constrain the wind's recombination timescale and geometry.
  • The method should be tested on archival high-cadence optical spectra of other flaring LMXBs; if inverted P-Cygni profiles appear whenever strong winds are present, rms spectroscopy becomes a standard outflow census tool.
  • A natural next step is to propagate uncertainties through the rms computation; the paper's figures do not display error bars, so quantifying them would define how faint a wind signature can be reliably claimed.
  • Time-dependent photoionisation wind models could be used to predict rms spectra directly, turning the empirical inverted-profile diagnostic into a quantitative probe of wind density, ionisation parameter, and mass-loss rate.
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

4 major / 5 minor

Summary. The paper presents the first optical fractional rms spectra of a low-mass X-ray binary, computed from high-cadence GTC spectroscopy of V404 Cyg during its 2015 outburst. The authors find that spectral lines generally appear as dips in the rms spectrum, that P-Cygni profiles in flux appear as inverted profiles in rms (enhanced blue variability and suppressed red variability), that these inverted profiles strengthen during flare-related segments, and that weak wind-related asymmetries can appear in rms even when the flux spectrum does not show a clear P-Cygni profile. They propose rms spectroscopy as a new diagnostic for wind outflows in accreting compact objects.

Significance. If the statistical significance is established, this is a valuable observational result: it extends the X-ray/UV rms-spectroscopy technique to optical LMXB spectra and suggests a new way to detect or confirm wind-related absorption in cases where the time-averaged flux profile is inconclusive. The paper uses a unique high-cadence dataset, applies a standard excess-variance estimator, and does not rely on fitted parameters or circular derivation. The central finding is an observational characterization, and the comparison with AGN ultra-fast outflow studies is a useful scientific framing. The main weakness is that the reported significance of the key rms features is not currently quantified.

major comments (4)
  1. [Section 3, Eq. (2)] The quoted uncertainty is not the uncertainty of the fractional rms; it is the fractional standard error of the mean flux, sqrt(sigma_err^2/N)/xbar. The uncertainty of an rms or variance estimate also includes the sampling variance of the variability itself; for N=10 spectra this is at least about 24% even for Gaussian white noise, and larger for red noise. Section 4.2 reports inverted-profile amplitudes of 'a few percent in S1 to nearly 10 percent in S5', so the S1-S7 features may be within sampling noise. Please replace Eq. (2) with a proper error propagation for the excess variance (e.g., the Vaughan et al. 2003 treatment or a bootstrap/Monte Carlo estimate) and show the resulting uncertainties.
  2. [Figures 1-4, captions] No error bars or confidence bands are shown on the rms spectra. The statement that 'statistical errors are smaller than the data points' refers to photon/measurement noise on the mean flux, not to the sampling uncertainty of the rms estimate. Since the rms continuum is not flat (Section 4.1 reports a blue slope on most days), the figures do not by themselves demonstrate that the blue excess / red deficit is a line-related inverted P-Cygni signature rather than a local manifestation of the continuum slope. Please show continuum-subtracted residual rms profiles, or confidence bands, at least for the short N=10 segments in Figure 3.
  3. [Section 5.1] The claim that features unrelated to the source 'do not leave an imprint in the rms spectra, nor do they mimic the behaviour of an inverted profile in rms' is central to the proposed diagnostic but is not supported by a null test. Interstellar and telluric features are constant by construction, whereas calibration or normalisation systematics could vary with the flare; the relevant test is an rms spectrum from a non-variable calibration region or a simulated constant line added to a variable continuum. Please add such a test or soften the claim accordingly.
  4. [Section 5] The interpretation that the rms behaviour is driven by photoionisation changes, via relative ion-abundance curves, is qualitative and exploratory. It is plausible but is not quantitatively tied to the observed variability amplitudes, densities, or ionisation parameters. This is acceptable as a discussion-level hypothesis, but the phrasing in the Conclusions ('suggesting that rapid changes in the ionisation state of the gas play a significant role') goes beyond what the present analysis can test.
minor comments (5)
  1. [Section 3, Eq. (2)] There is a typo: 'N is the number is the number of spectra' should read 'N is the number of spectra'.
  2. [Section 3] The text states that the rms is evaluated over frequencies of about 2e-2 Hz down to 1e-4 Hz. With roughly one spectrum per minute and segment lengths of about 50-120 minutes, the accessible band is approximately 1e-4 to 6e-3 Hz. Please correct or justify the upper frequency.
  3. [Section 3, Eq. (1)] The notation x2 for the squared average is confusing next to Eq. (2)'s xbar; please use a consistent notation such as xbar^2.
  4. [Section 4.2] The statement that 'the results did not change when we slightly varied the number of spectra per segment' is not supported by a quantitative test. Please specify the range of segment lengths tried and how the amplitudes or significances changed.
  5. [Section 1] There is a typo in the Introduction: 'makes a contribution to the to optical regime' should read 'contributes to the optical regime'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports empirical rms spectra and does not fit parameters or rename inputs as predictions.

full rationale

The paper is an observational study: it computes fractional rms spectra from existing high-cadence spectroscopy of V404 Cyg and describes their morphology. The central claims are empirical descriptions of the computed rms spectra, not derived predictions. Equation (1) defines the fractional rms estimator, but no parameter is fitted to a subset of the data and then presented as a prediction of a closely related quantity. The 'inverted P-Cygni' pattern is reported as an observed property of the rms spectra and explained post hoc in terms of the line components varying less or more than the adjacent continuum; this is a physical interpretation of the measured quantities, not a derivation that assumes the conclusion. The wind interpretation is anchored to prior work, especially Muñoz-Darias et al. (2016), which established the presence of wind signatures in the same dataset from flux spectra; those citations provide external observational context rather than importing an unverified uniqueness theorem or ansatz. Self-citation is present and normal for a group continuing to analyse its own dataset, but the rms spectra are new measurements and are not assumed by the cited papers, so the self-citations are not load-bearing in a circular sense. The paper also explicitly caveats its exploratory photoionisation interpretation in Section 5, noting that it is 'strongly dependent on several factors'. The main substantive concerns are statistical rather than circular: Equation (2) appears to give the standard error of the mean rather than the sampling uncertainty of the rms itself, and the short segments S1-S7 contain only 10 spectra each, so the quoted few-percent to ten-percent features may be dominated by sampling noise. These are correctness or robustness issues, not evidence of circular reasoning. Overall, no step in the paper's argument reduces by construction to its own inputs.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The analysis rests on standard time-series estimators and on the prior identification of winds in this dataset. No new free parameters are introduced, and the interpretive model of ionisation-driven variability is qualitative and not fitted to the data.

assumptions (3)
  • standard math The fractional rms as defined in Eq. (1) is an unbiased estimator of intrinsic variability after subtracting Poisson noise.
    Basis for the analysis; errors on rmsfrac follow Vaughan et al. (2003).
  • domain assumption The P-Cygni features in flux spectra of V404 Cygni are of wind origin.
    Established in Muñoz-Darias et al. (2016) and assumed when attributing rms features to winds (Section 5).
  • domain assumption The eight selected days and the seven segments of day 2 are representative of the variability phases relevant to wind signatures.
    The choice of days 1-8 from the campaign, and the split into blocks S1-S7, is based on lightcurve morphology (Table 1 and Section 4.2); the authors note that the low-frequency rms band shifts with segment length.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Accretion disc winds imprint distinct signatures in the optical variability spectrum of black hole transients." pith.science (2026). https://pith.science/paper/KTIZ3AEQ

@misc{pith2026250104087,
  author       = {Pith},
  title        = {Pith review of: Accretion disc winds imprint distinct signatures in the optical variability spectrum of black hole transients},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KTIZ3AEQ}},
  note         = {Machine review of arXiv:2501.04087}
}
read the original abstract

Quantifying the variability, measured as the root mean square (rms), of accreting systems as a function of energy is a powerful tool for constraining the physical properties of these objects. Here, we present the first application of this method to optical spectra of low-mass X-ray binaries. We use high-time-resolution data of the black hole transient V404 Cygni, obtained with the \textit{Gran Telescopio Canarias} during its 2015 outburst. During this event, conspicuous wind-related features, such as P-Cygni profiles, were detected in the flux spectra. We find that rms spectra are rich in spectral features, although they are typically morphologically different from their flux counterparts. Specifically, we typically observe absorption components in correspondence to the presence of emission lines in the flux spectra. Similarly, when analysing segments with significant variability in the optical flux, P-Cygni line profiles appear inverted in the rms spectra (i.e., enhanced variability in the blue-shifted region, accompanied by a decrease in that associated with the red component). We discuss the possible origin of these features, which resemble those found in other objects, such as active galactic nuclei. Finally, we highlight the potential of this technique for future searches for wind-type outflows in accreting compact objects.

Figures

Figures reproduced from arXiv: 2501.04087 by the authors.

Figure 1
Figure 1. Flux-averaged (top) and fractional rms (bottom) spectra for the eight epochs included in this study. In the top panel, we mark the main lines that will be analysed and discussed in this paper: He i lines at ∼ 5876, 6678 and 7065 Å, He ii-4686, Hα and Hβ. • The mean level of variability changes significantly over the campaign (i.e. day 1 to 8). It is as high as ∼ 80 per cent on day 2 and as low as ∼ 10 per cent on da… view at source ↗
Figure 2
Figure 2. Detailed rms and flux spectra from day 2 and day 6. The four panels show the flux (top) and fractional rms (bottom) spectra for day 2 (left) and day 6 (right). The top row presents the behaviour of He i-5876, while Hα is shown in the bottom row. The rms spectra show an inverted shape compared to the flux spectra, especially in the presence of P-Cygni profiles. Statistical errors are smaller than the data points. Ver… view at source ↗
Figure 3
Figure 3. Shorter-term evolution throughout day 2. The top panel shows the day 2 light curve and the intervals selected for calculating short-term rms spectra. The flux spectra for each segment are shown in the left column, with the corresponding rms spectra in the right panels. Dotted vertical lines mark the positions of the He i lines (at ∼5876, 6678, and 7065 Å), He ii 4686 Å, Hα, and Hβ. Interestingly, the He ii line show… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Detailed rms and flux spectra of Hα for day 7 (left) and day 8 (right). While the flux absorption in the blue part of the line is mild, the asymmetry in the rms spectrum is evident. S/N flux spectra (including those obtained by averaging several individual spectra), th…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 36 canonical work pages

  1. [1]

    & Hasinger, G

    Belloni, T. & Hasinger, G. 1990, A&A, 227, L33

    work page 1990

  2. [2]

    R., van der Klis, M., & van Paradijs, J

    Belloni, T., Méndez, M., King, A. R., van der Klis, M., & van Paradijs, J. 1997, ApJ, 479, L145

    work page 1997

  3. [3]

    Belloni, T. M. 2010, Lecture Notes in Physics, Berlin Springer Verlag, 794, 53

    work page 2010

  4. [4]

    M., Motta, S

    Belloni, T. M., Motta, S. E., & Muñoz-Darias, T. 2011, Bulletin of the Astro- nomical Society of India, 39, 409

    2011

  5. [5]

    2019, MNRAS, 488, 1356

    Casares, J., Muñoz-Darias, T., Mata Sánchez, D., et al. 2019, MNRAS, 488, 1356

    work page 2019

  6. [6]

    Castor, J. I. & Lamers, H. J. G. L. M. 1979, ApJS, 39, 481 Castro Segura, N., Knigge, C., Long, K. S., et al. 2022, Nature, 603, 52

    work page 1979

  7. [7]

    H., Buckley, D

    Charles, P., Matthews, J. H., Buckley, D. A. H., et al. 2019, MNRAS, 489, L47

    2019

  8. [8]

    & Fender, R

    Corbel, S. & Fender, R. P. 2002, ApJ, 573, L35 Cúneo, V . A., Muñoz-Darias, T., Jiménez-Ibarra, F., et al. 2023, A&A, 679, A85 Cúneo, V . A., Muñoz-Darias, T., Sánchez-Sierras, J., et al. 2020, MNRAS, 498, 25 Díaz Trigo, M., Altamirano, D., Dinçer, T., et al. 2018, A&A, 616, A23 Díaz Trigo, M., Migliari, S., Miller-Jones, J. C. A., et al. 2017, A&A, 600, A8

    work page 2002

Show all 45 references
  1. [9]

    Drew, J. E. 1997, in IAU Colloq. 163: Accretion Phenomena and Related Out- flows, ed. D. T. Wickramasinghe, G. V . Bicknell, & L. Ferrario (ASP Conf. Ser.), 465

    1997

  2. [10]

    J., Pounds, K., et al

    Edelson, R., Turner, T. J., Pounds, K., et al. 2002, ApJ, 568, 610

    2002

  3. [11]

    & Muñoz-Darias, T

    Fender, R. & Muñoz-Darias, T. 2016, 905, 905

    2016

  4. [12]

    P., Belloni, T

    Fender, R. P., Belloni, T. M., & Gallo, E. 2004, MNRAS, 355, 1105

    2004

  5. [13]

    P., Mooley, K

    Fender, R. P., Mooley, K. P., Motta, S. E., et al. 2023, MNRAS, 518, 1243

    2023

  6. [14]

    2023, MNRAS, 526, 526

    Fijma, S., Castro Segura, N., Degenaar, N., et al. 2023, MNRAS, 526, 526

    2023

  7. [15]

    W., Russell, D

    Gandhi, P., Blain, A. W., Russell, D. M., et al. 2011, ApJ, 740, L13

    2011

  8. [16]

    S., Byrom, J., et al

    Gandhi, P., Borowski, E. S., Byrom, J., et al. 2024, arXiv e-prints, arXiv:2406.18637

    2024 arXiv

  9. [17]

    Georganti, M., Knigge, C., Castro Segura, N., & Long, K. S. 2022, MNRAS, 511, 5385 Gierli´nski, M. & Zdziarski, A. A. 2005, MNRAS, 363, 1349

    2022

  10. [18]

    I., Haswell, C

    Hynes, R. I., Haswell, C. A., Cui, W., et al. 2003, MNRAS, 345, 292

    2003

  11. [19]

    L., Matzeu, G

    Igo, Z., Parker, M. L., Matzeu, G. A., et al. 2020, MNRAS, 493, 1088

    2020

  12. [20]

    2000, ApJ, 542, L33 Jiménez-Ibarra, F., Muñoz-Darias, T., Casares, J., Armas Padilla, M., & Corral-

    Janiuk, A., Czerny, B., & Siemiginowska, A. 2000, ApJ, 542, L33 Jiménez-Ibarra, F., Muñoz-Darias, T., Casares, J., Armas Padilla, M., & Corral-

    2000

  13. [21]

    Santana, J. M. 2019, MNRAS, 489, 489

    2019

  14. [22]

    Kallman, T. R. & McCray, R. 1982, ApJS, 50, 263

    1982

  15. [23]

    2016, Nature, 529, 54

    Kimura, M., Isogai, K., Kato, T., et al. 2016, Nature, 529, 54

    2016

  16. [24]

    L., Miller, J

    King, A. L., Miller, J. M., Raymond, J., Reynolds, M. T., & Morningstar, W. 2015, ApJ, 813, L37

    2015

  17. [25]

    Lamers, H. J. G. L. M. & Cassinelli, J. P. 1999, Introduction to Stellar Winds Mata Sánchez, D., Muñoz-Darias, T., Casares, J., et al. 2018, MNRAS, 481, 2646 Mata Sánchez, D., Muñoz-Darias, T., Casares, J., Huertas-Company, M., &

    1999

  18. [26]

    2023, MNRAS, 524, 338

    Panizo-Espinar, G. 2023, MNRAS, 524, 338

    2023

  19. [27]

    McClintock, J. E. & Remillard, R. A. 2006, Black hole binaries, ed. W. H. G. Lewin & M. van der Klis, 157–213

    2006

  20. [28]

    A., Miller-Jones, J

    Migliari, S., Tomsick, J. A., Miller-Jones, J. C. A., et al. 2010, ApJ, 710, 117

    2010

  21. [29]

    E., Kajava, J

    Motta, S. E., Kajava, J. J. E., Sánchez-Fernández, C., Giustini, M., & Kuulkers, E. 2017, MNRAS, 468, 981 Muñoz-Darias, T., Armas Padilla, M., Jiménez-Ibarra, F., et al. 2020, ApJ, 893, L19 Muñoz-Darias, T., Casares, J., Mata Sánchez, D., et al. 2016, Nature, 534, 75 Muñoz-Dar...

    2017

  22. [30]

    & Lee, J

    Neilsen, J. & Lee, J. C. 2009, Nature, 458, 481

    2009

  23. [31]

    A., & Lee, J

    Neilsen, J., Remillard, R. A., & Lee, J. C. 2011, ApJ, 737, 69

    2011

  24. [32]

    2022, A&A, 664, 664

    Panizo-Espinar, G., Armas Padilla, M., Muñoz-Darias, T., et al. 2022, A&A, 664, 664

    2022

  25. [33]

    L., Pinto, C., Fabian, A

    Parker, M. L., Pinto, C., Fabian, A. C., et al. 2017, Nature, 543, 83

    2017

  26. [34]

    O., Bianchi, S., et al

    Parra, M., Petrucci, P. O., Bianchi, S., et al. 2024, A&A, 681, 681

    2024

  27. [35]

    R., Fried, R., et al

    Patterson, J., Thorstensen, J. R., Fried, R., et al. 2001, PASP, 113, 72

    2001

  28. [36]

    L., et al

    Pinto, C., Alston, W., Parker, M. L., et al. 2018, MNRAS, 476, 1021

    2018

  29. [37]

    P., Begelman, M

    Ponti, G., Fender, R. P., Begelman, M. C., et al. 2012, MNRAS, 422, L11

    2012

  30. [38]

    M., Miller-Jones, J

    Russell, D. M., Miller-Jones, J. C. A., Maccarone, T. J., et al. 2011, ApJ, 739, L19

    2011

  31. [39]

    D., Soria, R., Miller-Jones, J

    Russell, T. D., Soria, R., Miller-Jones, J. C. A., et al. 2014, MNRAS, 439, 1390 Sánchez-Sierras, J. & Muñoz-Darias, T. 2020, A&A, 640, L3 Sánchez-Sierras, J., Muñoz-Darias, T., Motta, S. E., et al. 2023, A&A, 680, L16

    2014

  32. [40]

    & Vitello, P

    Shlosman, I. & Vitello, P. 1993, ApJ, 409, 372

    1993

  33. [41]

    J., Casella, P., Miller-Jones, J

    Tetarenko, A. J., Casella, P., Miller-Jones, J. C. A., et al. 2021, MNRAS, 504, 3862

    2021

  34. [42]

    S., & Uttley, P

    Vaughan, S., Edelson, R., Warwick, R. S., & Uttley, P. 2003, MNRAS, 345, 1271

    2003

  35. [43]

    M., Neilsen, J., Tetarenko, A

    Vincentelli, F. M., Neilsen, J., Tetarenko, A. J., et al. 2023, Nature, 615, 45

    2023

  36. [44]

    A., et al

    Wang, J., Kara, E., García, J. A., et al. 2024, ApJ, 963, 14

    2024

  37. [45]

    Zdziarski, A. A. & Gierli´nski, M. 2004, Progress of Theoretical Physics Supple- ment, 155, 99 Article number, page 8 of 8

    2004

Pith tools

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