pith. machine review for the scientific record. sign in

arxiv: 2604.16688 · v1 · submitted 2026-04-17 · 🌌 astro-ph.HE

Recognition: unknown

Persistence of the Millihertz X-ray Quasi-Periodic Oscillation in the Active Galactic Nucleus 1ES 1927+654

Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:02 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords quasi-periodic oscillationsactive galactic nucleiX-ray timing1ES 1927+654accretion diskXMM-NewtonNuSTARblack hole variability
0
0 comments X

The pith

The millihertz X-ray QPO in 1ES 1927+654 has plateaued at a constant frequency of 2.5 mHz.

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

The paper tracks the evolution of a repeating X-ray brightness variation in an active galactic nucleus that started changing frequency in 2022. New data from 2024 to 2026 show the variation has stopped drifting and now occurs at a steady rate of about 2.5 millihertz. Analyses of more than 900 cycles reveal no strong second harmonic in the power spectrum, a consistent soft lag between energy bands, and large flux swings that repeat in a stable dip-rise-fall shape. The same signal appears in NuSTAR observations at matching frequencies. These features test ideas about how matter orbits and interacts near the central black hole.

Core claim

In 1ES 1927+654 the X-ray QPO persists with its frequency now fixed near 2.5 mHz. The stacked power spectra lack a significant second harmonic. A soft lag is present at all frequencies and stays stable while the QPO frequency changed earlier. Extreme X-ray jumps reaching 80 percent of baseline flux continue with a fixed dip-rise-fall pattern. The QPO is detected for the first time in NuSTAR data at frequencies matching the XMM-Newton results.

What carries the argument

Stacked XMM-Newton power spectra combined with spectral-timing analysis of the QPO signal across epochs, which isolate the frequency plateau, absence of harmonics, and stability of the lag and flux pattern.

Load-bearing premise

The periodic signals seen in XMM-Newton and NuSTAR data across years represent continuation of the same physical QPO rather than separate or coincidental variability.

What would settle it

A future observation in which the QPO frequency shifts away from 2.5 mHz or the periodic signal disappears would show that the plateau is temporary.

Figures

Figures reproduced from arXiv: 2604.16688 by Adam Ingram, Andrew C. Fabian, Benny Trakhtenbrot, Christos Panagiotou, Ciro Pinto, Claudio Ricci, Dev R. Sadaula, Erin Kara, Giovanni Miniutti, Joheen Chakraborty, Margherita Giustini, Megan Masterson, Mitchell Begelman, Onic I. Shuvo, Peter Kosec, Riccardo Arcodia, Sibasish Laha, William N. Alston.

Figure 1
Figure 1. Figure 1: Evolution of the QPO frequency. Left: Evolution of the QPO frequency with time. The blue circles show XM￾M-Newton observations in the 2-10 keV band, with filled circles denoting significant detections (> 3σ) and open circles denoting marginal detections (2−3σ), as estimated using ∆AIC. The pink squares show joint fits to multiple XMM-Newton observations in a given epoch, which all show significant detectio… view at source ↗
Figure 2
Figure 2. Figure 2: Derivatives of the QPO frequency with respect to time (i.e., ˙fQPO and ¨fQPO) based on the best-fit bend￾ing power-law model for the QPO frequency evolution. The shaded pink (blue) region shows the 1σ confidence interval for the ˙fQPO ( ¨fQPO) evolution based on the covariance ma￾trix for the fit to the model. able to confidently say whether the QPO continues to strengthen with increasing energy above 10 k… view at source ↗
Figure 3
Figure 3. Figure 3: Existence of a QPO in the NuSTAR data from 2023-2026. The top panel of each plot shows the stacked NuSTAR PSD in the 3-10 keV band with 50s time bins. The error bars correspond to the standard error on the mean. The blue dotted line shows the broadband model, assuming only a power-law and constant. The pink dashed line shows the addition of a Lorentzian for the QPO on top of this broadband model. For both … view at source ↗
Figure 5
Figure 5. Figure 5: Stacked PSD of all XMM-Newton observations from July 2024-January 2026 in the 2-10 keV band (blue) and 0.3-2 keV band (pink). Each of the stacked PSDs are fit with a broken power-law broadband noise model and a Lorentzian for the QPO, the best fit of which are shown as dashed lines. The orange arrow shows the the fundamental QPO frequency as measured from the 2-10 keV PSD, and the blue and pink arrows show… view at source ↗
Figure 4
Figure 4. Figure 4: Evolution of the QPO properties with time, where the color coding and shapes match [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Lag spectra during the QPO phase of 1ES 1927+654, constructed with 10s binned light curves and adopting the convention that a positive lag indicates that the hard band lags behind the soft band. Top panel: LFS between the 0.3-1 keV and 1-4 keV bands. The gray points show the individual LFS measurements for the observations in which the QPO frequency significantly changed (i.e., those observations presented… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of the LFS in 1ES 1927+654 (black) with all observations from 2023-2026 to that of Ark 564 (blue) from [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Strong X-ray jumps on the QPO period in 1ES 1927+654. Left: 0.3-2 keV (blue) and 2-10 keV (pink) light curves for the observation from February 2023 with a 1.67 mHz QPO (top) and from January 2026 with a 2.48 mHz QPO (bottom). Both observations show repetitive sudden drops in the X-ray flux by > 20%, followed by a rapid increase of the X-ray flux. These occur on the QPO timescale, but do not repeat as regu… view at source ↗
Figure 9
Figure 9. Figure 9: Fractional variability of the 5 major jumps shown in the right panel of [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: 2-10 keV light curves (left) and PSDs (right) for all observations analyzed in this work. For visual clarity, the light curves are binned to 60s bins, with the shaded regions showing the uncertainties. The PSDs are computed with 20s light curves, and the pink vertical dashed line shows the frequency of the QPO, measured from Lorentzian fits. For ease of visual comparison, we fix the x- and y-axes limits t… view at source ↗
Figure 10
Figure 10. Figure 10: (continued) [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 10
Figure 10. Figure 10: (continued) [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Left: Detection significance of the QPO feature as a function of observation length. The trend is statistically significant (p < 0.05) and suggests that the detection significance increases with the duration of the observation. There are occasional non-detections where the observational length is expected to be long enough to reach a confident detection, thereby suggesting that the QPO has some level of i… view at source ↗
read the original abstract

1ES 1927+654 is an extreme active galactic nucleus (AGN) that has defied our canonical expectations for how AGN appear across the electromagnetic spectrum and how they vary on short timescales. In 2022, this source began showing a X-ray quasi-periodic oscillation (QPO) at mHz frequencies, along with a newly launched radio jet. Unlike the handful of other known AGN QPOs, the QPO in 1ES 1927+654 showed a significant frequency evolution, spanning from 0.9-2.4 mHz from 2022-2024. In this work, we present the last 1.5 years of monitoring with XMM-Newton (250 ks) up to January 2026, which reveals that the QPO persists but has plateaued at a constant frequency of approximately 2.5 mHz. We perform detailed spectral-timing analyses on this exquisite dataset, consisting of over 900 QPO cycles, more than any AGN QPO to date. Our main findings are: (1) the stacked XMM-Newton power spectra shows no significant second harmonic, (2) a soft (reverberation-like) lag is observed at all frequencies and remains remarkably stable even as the QPO frequency evolved from 2022-2024, and (3) extreme X-ray jumps on the QPO period (up to ~80% baseline flux) persist to present day with a remarkably stable dip-rise-fall pattern. Finally, we also detect the first AGN QPO in NuSTAR observations, which is present from 2023 to 2026 at frequencies consistent with the XMM-Newton detections. While we explore models for eclipses and coupled disk-corona behavior to simultaneously explain the lags, dips, and QPO, these new observations strain such models.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 3 minor

Summary. The manuscript reports continued monitoring of the millihertz X-ray QPO in the AGN 1ES 1927+654. After frequency evolution from 0.9-2.4 mHz (2022-2024), the QPO has persisted and plateaued at ~2.5 mHz over the subsequent 1.5 years. This is based on 250 ks XMM-Newton data (>900 cycles) plus NuSTAR observations, with stacked power spectra showing no significant second harmonic, a stable soft reverberation-like lag at all frequencies, persistent extreme (~80% baseline) dip-rise-fall flux jumps on the QPO period, and the first NuSTAR detection of an AGN QPO at consistent frequencies. Models for eclipses or coupled disk-corona behavior are explored but described as strained by the new observations.

Significance. If the observational results hold, this constitutes the longest temporal baseline and highest cycle count (>900) for any AGN QPO, providing a unique dataset for testing QPO mechanisms. Strengths include the large exposure, consistent multi-instrument detection across XMM-Newton and NuSTAR, explicit statements on the absence of a second harmonic, and the remarkable stability of the soft lag even through prior frequency evolution. These features offer direct constraints on accretion flow models and reverberation in AGN, with the plateau and persistent pattern challenging simple evolutionary or eclipse scenarios.

major comments (1)
  1. The central claim of persistence and a true frequency plateau at 2.5 mHz rests on cross-epoch identification of the signal as the continuation of the 2022-2024 feature. While frequencies are consistent and the lag/pattern stability provides supporting evidence, the manuscript would benefit from an explicit statistical test (e.g., a stationarity analysis or drift-rate upper limit with uncertainties) in the power-spectrum section to rule out the possibility of unrelated variability near 2.5 mHz or an undetected slow residual evolution.
minor comments (3)
  1. The abstract and results sections state the QPO 'persists but has plateaued'; a dedicated paragraph summarizing the frequency measurements and their uncertainties for each epoch (2022-2024 vs. 2024-2026) would improve clarity on the plateau claim.
  2. Figure captions and the NuSTAR detection section should explicitly note the energy bands used for the power spectra and lag analysis to allow direct comparison with prior XMM-Newton results.
  3. The model exploration (eclipses and disk-corona coupling) is presented as strained; adding a short table comparing predicted vs. observed lag amplitudes or harmonic content would make this assessment more quantitative without altering the observational focus.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and for the constructive suggestion. We address the major comment below.

read point-by-point responses
  1. Referee: The central claim of persistence and a true frequency plateau at 2.5 mHz rests on cross-epoch identification of the signal as the continuation of the 2022-2024 feature. While frequencies are consistent and the lag/pattern stability provides supporting evidence, the manuscript would benefit from an explicit statistical test (e.g., a stationarity analysis or drift-rate upper limit with uncertainties) in the power-spectrum section to rule out the possibility of unrelated variability near 2.5 mHz or an undetected slow residual evolution.

    Authors: We agree that an explicit statistical test would strengthen the central claim. In the revised manuscript we will add a stationarity analysis of the power spectra across the full 2022–2026 baseline in the power-spectrum section. This will include a quantitative upper limit on any residual frequency drift (with uncertainties) derived from the multi-epoch data, thereby directly addressing the possibility of unrelated variability near 2.5 mHz or undetected slow evolution. The existing lag and flux-pattern stability will be retained as supporting evidence but will no longer be the sole basis for the plateau interpretation. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational report of measured frequencies, lags, and flux patterns from telescope data

full rationale

The paper's central claims rest on direct measurements of QPO frequency (~2.5 mHz plateau), absence of second harmonic in stacked power spectra, stable soft lags, and persistent dip-rise-fall flux patterns extracted from 250 ks XMM-Newton and NuSTAR observations spanning 2023-2026. These quantities are obtained via standard periodogram and lag analysis on public data without any fitted parameters that are then renamed as predictions. Model explorations for eclipses or disk-corona coupling are explicitly described as strained and not used to derive the reported frequencies or stability. No self-citations serve as load-bearing uniqueness theorems, and no ansatz or renaming of known results occurs. The analysis is self-contained against external data benchmarks with >900 cycles providing independent verification.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Observational timing study; relies on standard Fourier analysis assumptions for periodicity detection and cross-spectrum lag estimation. No new free parameters, axioms beyond domain-standard methods, or invented entities are introduced.

axioms (2)
  • standard math Standard assumptions of Fourier-based power spectrum and cross-spectrum analysis for detecting quasi-periodic signals in X-ray light curves
    Invoked implicitly when reporting stacked power spectra, absence of second harmonic, and frequency measurements.
  • domain assumption The observed soft lags are reverberation-like and can be compared across epochs without additional geometric modeling
    Used when stating lag stability despite frequency evolution.

pith-pipeline@v0.9.0 · 5727 in / 1503 out tokens · 43025 ms · 2026-05-10T07:02:13.726414+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

88 extracted references · 87 canonical work pages

  1. [1]

    A., & Klu´ zniak, W

    Abramowicz, M. A., & Klu´ zniak, W. 2001, A&A, 374, L19, doi: 10.1051/0004-6361:20010791

  2. [2]

    A new look at the statistical model identification.IEEE Transactions on Automatic Control19, 716–723 (1974)

    Akaike, H. 1974, IEEE Transactions on Automatic Control, 19, 716, doi: 10.1109/TAC.1974.1100705

  3. [3]

    2014, MNRAS, 445, L16, doi: 10.1093/mnrasl/slu127

    Middleton, M. 2014, MNRAS, 445, L16, doi: 10.1093/mnrasl/slu127

  4. [4]

    N., Fabian, A

    Alston, W. N., Fabian, A. C., Buisson, D. J. K., et al. 2019, MNRAS, 482, 2088, doi: 10.1093/mnras/sty2527

  5. [5]

    N., Pinto, C., Barret, D., et al

    Alston, W. N., Pinto, C., Barret, D., et al. 2021, MNRAS, 505, 3722, doi: 10.1093/mnras/stab1473

  6. [6]

    2021, Nature, 592, 704, doi: 10.1038/s41586-021-03394-6

    Arcodia, R., Merloni, A., Nandra, K., et al. 2021, Nature, 592, 704, doi: 10.1038/s41586-021-03394-6 Ar´ evalo, P., & Uttley, P. 2006, MNRAS, 367, 801, doi: 10.1111/j.1365-2966.2006.09989.x

  7. [7]

    I., & Middleton, M

    Ashton, D. I., & Middleton, M. J. 2021, MNRAS, 501, 5478, doi: 10.1093/mnras/staa4024

  8. [8]

    I., & Middleton, M

    Ashton, D. I., & Middleton, M. J. 2022, MNRAS, 513, 5245, doi: 10.1093/mnras/stac1122 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaboration, Price-Whel...

  9. [9]

    & Huppenkothen, D

    Bachetti, M., Harrison, F. A., Cook, R., et al. 2015, ApJ, 800, 109, doi: 10.1088/0004-637X/800/2/109

  10. [10]

    Barros, S. C. C., Marsh, T. R., Dhillon, V. S., et al. 2007, MNRAS, 374, 1334, doi: 10.1111/j.1365-2966.2006.11244.x

  11. [11]

    2022, MNRAS, 515, 2099, doi: 10.1093/mnras/stac1922

    Bellavita, C., Garc´ ıa, F., M´ endez, M., & Karpouzas, K. 2022, MNRAS, 515, 2099, doi: 10.1093/mnras/stac1922

  12. [12]

    2002, ApJ, 572, 392, doi: 10.1086/340290

    Belloni, T., Psaltis, D., & van der Klis, M. 2002, ApJ, 572, 392, doi: 10.1086/340290

  13. [13]

    2012, MNRAS, 427, 127, doi: 10.1111/j.1365-2966.2012.21948.x

    Belloni, T. M., Sanna, A., & M´ endez, M. 2012, MNRAS, 426, 1701, doi: 10.1111/j.1365-2966.2012.21634.x

  14. [14]

    iScience , keywords =

    Cackett, E. M., Bentz, M. C., & Kara, E. 2021, iScience, 24, 102557, doi: 10.1016/j.isci.2021.102557

  15. [15]

    2023, MNRAS, 526, 2331, doi: 10.1093/mnras/stad2877

    Cao, X., You, B., & Wei, X. 2023, MNRAS, 526, 2331, doi: 10.1093/mnras/stad2877

  16. [16]

    2021, ApJL, 921, L40, doi: 10.3847/2041-8213/ac313b

    Chakraborty, J., Kara, E., Masterson, M., et al. 2021, ApJL, 921, L40, doi: 10.3847/2041-8213/ac313b

  17. [17]

    2024, ApJ, 965, 12, doi: 10.3847/1538-4357/ad2941

    Chakraborty, J., Arcodia, R., Kara, E., et al. 2024, ApJ, 965, 12, doi: 10.3847/1538-4357/ad2941

  18. [18]

    2016, MNRAS, 459, 3963, doi: 10.1093/mnras/stw878

    Emmanoulopoulos, D. 2016, MNRAS, 459, 3963, doi: 10.1093/mnras/stw878

  19. [19]

    Corcoran, M. F. 2005, AJ, 129, 2018, doi: 10.1086/428756

  20. [20]

    2006, MNRAS, 366, 689, doi: 10.1111/j.1365-2966.2005.09908.x

    Crummy, J., Fabian, A. C., Gallo, L., & Ross, R. R. 2006, MNRAS, 365, 1067, doi: 10.1111/j.1365-2966.2005.09844.x

  21. [21]

    2022, PhRvD, 105, 103010, doi: 10.1103/PhysRevD.105.103010 De Marco, B., Ponti, G., Cappi, M., et al

    Davelaar, J., & Haiman, Z. 2022, PhRvD, 105, 103010, doi: 10.1103/PhysRevD.105.103010 De Marco, B., Ponti, G., Cappi, M., et al. 2013, MNRAS, 431, 2441, doi: 10.1093/mnras/stt339 D´ ıaz Trigo, M., Parmar, A. N., Boirin, L., M´ endez, M., &

  22. [22]

    Kaastra, J. S. 2006, A&A, 445, 179, doi: 10.1051/0004-6361:20053586

  23. [23]

    C., Lohfink, A., Kara, E., et al

    Fabian, A. C., Lohfink, A., Kara, E., et al. 2015, MNRAS, 451, 4375, doi: 10.1093/mnras/stv1218

  24. [24]

    , year = 2009, month = may, volume =

    Fabian, A. C., Zoghbi, A., Ross, R. R., et al. 2009, Nature, 459, 540, doi: 10.1038/nature08007

  25. [25]

    2023, A&A, 675, A100, doi: 10.1051/0004-6361/202346565

    Franchini, A., Bonetti, M., Lupi, A., et al. 2023, A&A, 675, A100, doi: 10.1051/0004-6361/202346565

  26. [26]

    C., MacMackin, C., Vasudevan, R., et al

    Gallo, L. C., MacMackin, C., Vasudevan, R., et al. 2013, MNRAS, 433, 421, doi: 10.1093/mnras/stt735

  27. [27]

    2023, ApJ, 955, 3, doi: 10.3847/1538-4357/aced92 Gierli´ nski, M., Middleton, M., Ward, M., & Done, C

    Ghosh, R., Laha, S., Meyer, E., et al. 2023, ApJ, 955, 3, doi: 10.3847/1538-4357/aced92 Gierli´ nski, M., Middleton, M., Ward, M., & Done, C. 2008, Nature, 455, 369, doi: 10.1038/nature07277

  28. [28]

    Giustini, M., Miniutti, G., & Saxton, R. D. 2020, A&A, 636, L2, doi: 10.1051/0004-6361/202037610 Gonz´ alez-Mart´ ın, O., & Vaughan, S. 2012, A&A, 544, A80, doi: 10.1051/0004-6361/201219008

  29. [29]

    F., Russell, C

    Hamaguchi, K., Corcoran, M. F., Russell, C. M. P., et al. 2014, ApJ, 784, 125, doi: 10.1088/0004-637X/784/2/125

  30. [30]

    2010 , month = jul, journal =

    Heil, L. M., & Vaughan, S. 2010, MNRAS, 405, L86, doi: 10.1111/j.1745-3933.2010.00864.x

  31. [31]

    2025, ApJ, 993, 186, doi: 10.3847/1538-4357/ae07ca

    Huang, X., Linial, I., & Jiang, Y.-F. 2025, ApJ, 993, 186, doi: 10.3847/1538-4357/ae07ca

  32. [32]

    E., Aigrain, S., & Karastergiou, A

    Ingram, A., Motta, S. E., Aigrain, S., & Karastergiou, A. 2021, MNRAS, 503, 1703, doi: 10.1093/mnras/stab609

  33. [33]

    2024, ApJ, 968, 76, doi: 10.3847/1538-4357/ad3faf

    Ingram, A., Bollemeijer, N., Veledina, A., et al. 2024, ApJ, 968, 76, doi: 10.3847/1538-4357/ad3faf

  34. [34]

    Ingram and Sara E

    Ingram, A. R., & Motta, S. E. 2019, NewAR, 85, 101524, doi: 10.1016/j.newar.2020.101524

  35. [35]

    Kaaret, P., Feng, H., & Roberts, T. P. 2017, ARA&A, 55, 303, doi: 10.1146/annurev-astro-091916-055259

  36. [36]

    N., Fabian, A

    Kara, E., Alston, W. N., Fabian, A. C., et al. 2016, MNRAS, 462, 511, doi: 10.1093/mnras/stw1695

  37. [37]

    F., Fabian, A

    Kara, E., Steiner, J. F., Fabian, A. C., et al. 2019, Nature, 565, 198, doi: 10.1038/s41586-018-0803-x

  38. [38]

    J., et al

    Kara, E., Pinto, C., Walton, D. J., et al. 2020, MNRAS, 491, 5172, doi: 10.1093/mnras/stz3318

  39. [39]

    S., White, S

    Kotov, O., Churazov, E., & Gilfanov, M. 2001, MNRAS, 327, 799, doi: 10.1046/j.1365-8711.2001.04769.x

  40. [40]

    M., Davelaar, J., Haiman, Z., et al

    Krauth, L. M., Davelaar, J., Haiman, Z., et al. 2024, PhRvD, 109, 103014, doi: 10.1103/PhysRevD.109.103014

  41. [41]

    1998, ApJ, 494, 753, doi: 10.1086/305248

    Kuulkers, E., Wijnands, R., Belloni, T., et al. 1998, ApJ, 494, 753, doi: 10.1086/305248

  42. [42]

    2022, ApJ, 931, 5, doi: 10.3847/1538-4357/ac63aa

    Laha, S., Meyer, E., Roychowdhury, A., et al. 2022, ApJ, 931, 5, doi: 10.3847/1538-4357/ac63aa

  43. [43]

    T., Sadaula, D

    Laha, S., Meyer, E. T., Sadaula, D. R., et al. 2025, ApJ, 981, 125, doi: 10.3847/1538-4357/adaea0

  44. [44]

    2025, STELA: Sampling Time for Even Lightcurve Analysis Toolkit,, Astrophysics Source Code Library, record ascl:2509.001 http://ascl.net/2509.001

    Lewin, C. 2025, STELA: Sampling Time for Even Lightcurve Analysis Toolkit,, Astrophysics Source Code Library, record ascl:2509.001 http://ascl.net/2509.001

  45. [45]

    C., Ricci, C., et al

    Li, R., Ho, L. C., Ricci, C., et al. 2022, ApJ, 933, 70, doi: 10.3847/1538-4357/ac714a

  46. [46]

    Linial, I., & Metzger, B. D. 2023, ApJ, 957, 34, doi: 10.3847/1538-4357/acf65b L´ opez-Barquero, V., Jenkins, A., Reynolds, C. S., &

  47. [47]

    2025, arXiv e-prints, arXiv:2512.09026, doi: 10.48550/arXiv.2512.09026

    Fabian, A. 2025, arXiv e-prints, arXiv:2512.09026, doi: 10.48550/arXiv.2512.09026

  48. [48]

    Lyubarskii, Y. E. 1997, MNRAS, 292, 679, doi: 10.1093/mnras/292.3.679

  49. [49]

    2003, ApJ, 598, 935, doi: 10.1086/379103

    Markowitz, A., Edelson, R., & Vaughan, S. 2003, ApJ, 598, 935, doi: 10.1086/379103

  50. [50]

    , keywords =

    Marsh, T. R., & Steeghs, D. 2002, MNRAS, 331, L7, doi: 10.1046/j.1365-8711.2002.05346.x

  51. [51]

    2022, ApJ, 934, 35, doi: 10.3847/1538-4357/ac76c0

    Masterson, M., Kara, E., Ricci, C., et al. 2022, ApJ, 934, 35, doi: 10.3847/1538-4357/ac76c0

  52. [52]

    2025, Nature, 638, 370, doi: 10.1038/s41586-024-08385-x The Millihertz X-ray QPO in 1ES 1927+65421

    Masterson, M., Kara, E., Panagiotou, C., et al. 2025, Nature, 638, 370, doi: 10.1038/s41586-024-08385-x The Millihertz X-ray QPO in 1ES 1927+65421

  53. [53]

    Fender, R. P. 2006, Nature, 444, 730, doi: 10.1038/nature05389

  54. [54]

    Mason, K. O. 2004, MNRAS, 348, 783, doi: 10.1111/j.1365-2966.2004.07376.x

  55. [55]

    T., Laha, S., Shuvo, O

    Meyer, E. T., Laha, S., Shuvo, O. I., et al. 2025, ApJL, 979, L2, doi: 10.3847/2041-8213/ad8651

  56. [56]

    2008, MNRAS, 388, 1803, doi: 10.1111/j.1365-2966.2008.13522.x 30

    Schurch, N. 2009, MNRAS, 394, 250, doi: 10.1111/j.1365-2966.2008.14255.x

  57. [57]

    D., Giustini, M., et al

    Miniutti, G., Saxton, R. D., Giustini, M., et al. 2019, Nature, 573, 381, doi: 10.1038/s41586-019-1556-x

  58. [58]

    H., Remillard, R

    Morgan, E. H., Remillard, R. A., & Greiner, J. 1997, ApJ, 482, 993, doi: 10.1086/304191

  59. [59]

    E., Belloni, T

    Motta, S. E., Belloni, T. M., Stella, L., Mu˜ noz-Darias, T., & Fender, R. 2014a, MNRAS, 437, 2554, doi: 10.1093/mnras/stt2068

  60. [60]

    E., Munoz-Darias, T., Sanna, A., et al

    Motta, S. E., Munoz-Darias, T., Sanna, A., et al. 2014b, MNRAS, 439, L65, doi: 10.1093/mnrasl/slt181

  61. [61]

    2012, ApJL, 757, L24, doi: 10.1088/2041-8205/757/2/L24

    Nixon, C., King, A., Price, D., & Frank, J. 2012, ApJL, 757, L24, doi: 10.1088/2041-8205/757/2/L24

  62. [62]

    Nowak, M. A. 2000, MNRAS, 318, 361, doi: 10.1046/j.1365-8711.2000.03668.x

  63. [63]

    2018, A&A, 610, A37, doi: 10.1051/0004-6361/201731841

    Panagiotou, C., & Walter, R. 2018, A&A, 610, A37, doi: 10.1051/0004-6361/201731841

  64. [64]

    R., Remillard, R

    Pasham, D. R., Remillard, R. A., Fragile, P. C., et al. 2019, Science, 363, 531, doi: 10.1126/science.aar7480

  65. [65]

    2017, MNRAS, 468, 2865, doi: 10.1093/mnras/stx641

    Pinto, C., Alston, W., Soria, R., et al. 2017, MNRAS, 468, 2865, doi: 10.1093/mnras/stx641

  66. [66]

    J., et al

    Pinto, C., Soria, R., Walton, D. J., et al. 2021, MNRAS, 505, 5058, doi: 10.1093/mnras/stab1648

  67. [67]

    M., & Corcoran, M

    Pittard, J. M., & Corcoran, M. F. 2002, A&A, 383, 636, doi: 10.1051/0004-6361:20020025

  68. [68]

    Raj, A., & Nixon, C. J. 2021, ApJ, 909, 82, doi: 10.3847/1538-4357/abdc25

  69. [69]

    A., Morgan, E

    Remillard, R. A., Morgan, E. H., McClintock, J. E., Bailyn, C. D., & Orosz, J. A. 1999, ApJ, 522, 397, doi: 10.1086/307606

  70. [70]

    A., Muno, M

    Remillard, R. A., Muno, M. P., McClintock, J. E., & Orosz, J. A. 2002, ApJ, 580, 1030, doi: 10.1086/343791

  71. [71]

    , keywords =

    Ricci, C., Kara, E., Loewenstein, M., et al. 2020, ApJL, 898, L1, doi: 10.3847/2041-8213/ab91a1

  72. [72]

    2021, ApJS, 255, 7, doi: 10.3847/1538-4365/abe94b

    Ricci, C., Loewenstein, M., Kara, E., et al. 2021, ApJS, 255, 7, doi: 10.3847/1538-4365/abe94b

  73. [73]

    , keywords =

    Scepi, N., Begelman, M. C., & Dexter, J. 2021, MNRAS, 502, L50, doi: 10.1093/mnrasl/slab002

  74. [74]

    2025, ApJL, 995, L30, doi: 10.3847/2041-8213/ae2464

    Shui, Q.-C., Zhang, S., Zhang, S.-N., et al. 2025, ApJL, 995, L30, doi: 10.3847/2041-8213/ae2464

  75. [75]

    2009, MNRAS, 398, 2122, doi: 10.1111/j.1365-2966.2009.15261.x

    Sobolewska, M. A., & Papadakis, I. E. 2009, MNRAS, 399, 1597, doi: 10.1111/j.1365-2966.2009.15382.x

  76. [76]

    S., Wilkins, D

    Taylor, C. S., Wilkins, D. R., & Allen, S. W. 2025, ApJ, 987, 135, doi: 10.3847/1538-4357/ade23d

  77. [77]

    , keywords =

    Trakhtenbrot, B., Arcavi, I., MacLeod, C. L., et al. 2019, ApJ, 883, 94, doi: 10.3847/1538-4357/ab39e4

  78. [78]

    Wilkins, D. R. 2014, A&A Rv, 22, 72, doi: 10.1007/s00159-014-0072-0

  79. [79]

    Uttley, P., & McHardy, I. M. 2001, MNRAS, 323, L26, doi: 10.1046/j.1365-8711.2001.04496.x

  80. [80]

    2006, MNRAS, 366, 689, doi: 10.1111/j.1365-2966.2005.09908.x

    Uttley, P., McHardy, I. M., & Vaughan, S. 2005, MNRAS, 359, 345, doi: 10.1111/j.1365-2966.2005.08886.x

Showing first 80 references.