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arxiv: 2606.22559 · v1 · pith:DRISVBSYnew · submitted 2026-06-21 · 🌌 astro-ph.GA · astro-ph.HE

Migration Traps as Variability Attractors: Optical/UV Signatures of Embedded Stellar-Mass Black Holes in Active Galactic Nucleus Disks

Pith reviewed 2026-06-26 10:10 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.HE
keywords AGN variabilitystellar-mass black holesmigration trapsmagnetic reconnectionaccretion disksoptical/UV variabilitystructure functionslag-wavelength relation
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The pith

Embedded stellar-mass black holes in AGN disks pile up at migration traps and drive excess short-timescale optical/UV variability through stochastic reconnection heating.

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

The paper examines whether stellar-mass black holes embedded in active galactic nucleus disks can produce detectable optical and ultraviolet variability signatures via migration-trap-driven magnetic heating. Migration traps cause these black holes to accumulate at preferred radii, triggering localized stochastic magnetic reconnection that heats the disk atmosphere in a self-regulated way. The process yields stronger variability on short timescales, flattened short-term structure functions, and departures from the standard time-lag versus wavelength scaling expected for thin accretion disks. A sympathetic reader would care because the mechanism supplies a physical link between otherwise hidden compact-object populations and observable continuum fluctuations without requiring changes in the overall accretion rate.

Core claim

By coupling a one-dimensional sBH population synthesis model with a corona-heated accretion-disk reprocessing variability framework, migration traps concentrate sBHs at preferred radii and generate localized, stochastic reconnection heating. The resulting heating is self-regulated: sBH pile-ups enhance the reconnection rate, while gap opening reduces the local gas density and partially suppresses the reconnection power. This heating produces excess short-timescale optical/UV variability, flattened short-term structure functions, and deviations from the standard τ∝λ^{4/3} lag-wavelength relation. These signatures are strongest at low-to-moderate Eddington ratios.

What carries the argument

Migration traps (torque-balance radii) that concentrate sBHs and drive localized stochastic magnetic reconnection heating, implemented via the coupling of a 1D population synthesis model to a corona-heated reprocessing framework.

If this is right

  • Excess short-timescale optical/UV variability appears in AGN disks.
  • Short-term structure functions become flattened.
  • The lag-wavelength relation deviates from τ∝λ^{4/3}.
  • Signatures reach maximum strength at low-to-moderate Eddington ratios and supply indirect evidence for embedded sBH populations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Existing long-term AGN light-curve archives could be re-analyzed for the predicted short-timescale excess and lag deviations to test the mechanism directly.
  • The self-regulated heating sets an upper limit on local sBH density once gap opening becomes important, offering a way to constrain compact-object populations without resolving individual objects.
  • If the variability channel operates, it could contribute to the observed diversity of AGN continuum behavior at wavelengths where coronal reprocessing alone under-predicts the amplitude.

Load-bearing premise

The assumption that sBHs migrate efficiently toward torque-balance radii and accumulate at migration traps in numbers large enough to produce observable reconnection heating, without gap opening fully suppressing the local gas density and reconnection power.

What would settle it

Multi-wavelength monitoring campaigns of AGN at low-to-moderate Eddington ratios that show neither excess short-timescale optical/UV variability nor deviations from the standard τ∝λ^{4/3} lag-wavelength relation would falsify the predicted signatures of sBH-driven reconnection heating.

Figures

Figures reproduced from arXiv: 2606.22559 by Da-Bin Lin, Jian-Min Wang, Jing-Tong Xing, Mouyuan Sun, Shuying Zhou, Tong Liu, Ya-Ping Li, Zhen-Yi Cai.

Figure 1
Figure 1. Figure 1: Migration regime maps in the M-R phase space for different Eddington ratios. Left panel: ˙m = 0.02; middle panel: m˙ = 0.05; right panel: ˙m = 0.20. The color regions represent the dominant migration states: blue/light blue indicates inward migration (including Type I migration and transition zone), yellow/orange indicates accretion-driven outward migration (and transition zone), and deep red indicates inw… view at source ↗
Figure 2
Figure 2. Figure 2: Radial evolution of the sBH surface number density and surface mass density over 5×105 yr. Top panels: ˙m = 0.05; bottom panels: ˙m = 0.20. In the moderate-accretion model, a secondary peak forms near log10(R/RS) ≃ 1.5, corresponding to the migration trap identified in [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Radial profile of the sBH heating ratio Φ(R, t) = Q 0 sBH(R, t)/Q0 vis(R) for different accretion rates and sBH population ages. Colors denote different accretion rates, ˙m = 0.02, 0.05, and 0.20, while line styles denote im￾ported population times timp = 102 , 5 × 104 , and 5 × 105 yr. At early times, the sBH population had not yet accumu￾lated efficiently and Φ ≪ 1 over most radii. As migration proceeds,… view at source ↗
Figure 4
Figure 4. Figure 4: Temporal evolution of the disk thermal response and multi-band light curves at ˙m = 0.05. The left panel shows an sBH-quiescent stage, while the right panel shows a late sBH-active stage after the embedded population has accumulated near the migration trap. From top to bottom, the panels show the background magnetic heating Q + mc, the mid-plane temperature Tc, the effective temperature Teff , and the synt… view at source ↗
Figure 5
Figure 5. Figure 5: Statistical variability diagnostics of the simulated 3000 ˚A light curves as functions of Eddington ratio and imported sBH population age. The upper-left panel shows the effective DRW damping timescale obtained by fitting the DRW structure– function form over ∆t ≈ 100–4000 days (100 days up to the per-segment maximum). The red dotted curve and shaded region show the observed Ren et al. (2024) relation and … view at source ↗
Figure 6
Figure 6. Figure 6: Inter-band lag and coherence diagnostics for the simulated continuum light curves as functions of the Eddington ratio ˙m and the imported sBH population age timp. Marker style encodes timp: 100 (circles), 102 (squares), 5 × 104 (diamonds), and 5 × 105 yr (triangles); the timp = 100 yr case is the CHAR-like reference state. Upper left: lag between 5100 ˚A and 3000 ˚A, measured from the coherence-weighted ph… view at source ↗
Figure 7
Figure 7. Figure 7: Sensitivity of the variability diagnostics to the gap-depletion floor fgap,min, for a representative low-accre￾tion, late-time model ( ˙m = 0.02, timp = 5 × 105 yr). (a) 3000 ˚A structure function for fgap,min = 0.2, 0.5, 0.9, and 1.0. A deeper gap (smaller fgap,min) lowers the thermal in￾ertia of the depleted annulus and raises the SF at short and intermediate time separations, while all models converge t… view at source ↗
read the original abstract

We investigate whether embedded stellar-mass black holes (sBHs) in active galactic nucleus (AGN) disks can leave observable optical/UV variability signatures through migration-trap-driven magnetic heating. This mechanism operates when sBHs migrating toward torque-balance radii pile up near migration traps, triggering localized, stochastic magnetic reconnection that heats the disk atmosphere. It is potentially important because it provides a physical source of non-coronal disk heating and directly links optical/UV continuum variability to otherwise hidden compact-object populations. By coupling a one-dimensional sBH population synthesis model with a corona-heated accretion-disk reprocessing variability framework, we show that migration traps concentrate sBHs at preferred radii and generate localized, stochastic reconnection heating. The resulting heating is self-regulated: sBH pile-ups enhance the reconnection rate, while gap opening reduces the local gas density and partially suppresses the reconnection power. This heating produces excess short-timescale optical/UV variability, flattened short-term structure functions, and deviations from the standard $\tau\propto\lambda^{4/3}$ lag-wavelength relation, which describes the time delay between variability at different wavelengths for a standard thin accretion disk. These signatures are strongest at low-to-moderate Eddington ratios, and related observations could provide indirect evidence for embedded compact-object populations in AGN disks.

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

2 major / 2 minor

Summary. The manuscript investigates whether embedded stellar-mass black holes (sBHs) in AGN disks produce observable optical/UV variability signatures via migration-trap-driven magnetic reconnection heating. It couples a one-dimensional sBH population synthesis model with a corona-heated accretion-disk reprocessing variability framework to argue that sBH pile-ups at torque-balance radii generate localized, stochastic reconnection heating that is self-regulated by gap opening; this is claimed to yield excess short-timescale variability, flattened short-term structure functions, and deviations from the standard τ∝λ^{4/3} lag-wavelength relation, with the effects strongest at low-to-moderate Eddington ratios.

Significance. If the quantitative outputs of the coupled model confirm that net reconnection heating after gap-opening suppression exceeds background coronal levels sufficiently to drive the claimed variability signatures, the work would link otherwise hidden compact-object populations to AGN continuum variability and provide falsifiable predictions for structure functions and lag relations.

major comments (2)
  1. [Results (coupling of population synthesis and reprocessing framework)] The central claim requires that the 1D population synthesis produces net reconnection heating (after self-regulation by gap opening) that exceeds background levels enough to generate observable excess variability and lag deviations. The manuscript states this outcome but supplies no numerical results from the synthesis, such as trapped sBH column, reconnection luminosity relative to coronal heating, or surface-density thresholds at low-to-moderate Eddington ratios, preventing verification that the signatures are actually produced.
  2. [Methods (population synthesis and reprocessing coupling)] The self-regulated heating description is presented as an independent output that produces flattened structure functions and deviations from τ∝λ^{4/3}. Without explicit equations or parameter values for the reconnection rate, the gap-opening suppression factor, or the resulting heating profile in the reprocessing framework, it is unclear whether these deviations are robust predictions or shaped by choices in the population synthesis.
minor comments (2)
  1. The abstract and text use standard notation for the lag-wavelength relation but would benefit from a one-sentence reminder of its thin-disk origin for broader accessibility.
  2. A table listing the key parameters of the 1D population synthesis model (e.g., migration rates, trap radii, Eddington ratio ranges) would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive report and for highlighting the need for more explicit quantitative support. We agree that the central claims would be strengthened by additional numerical outputs and model equations, and we will revise the manuscript to incorporate these elements.

read point-by-point responses
  1. Referee: [Results (coupling of population synthesis and reprocessing framework)] The central claim requires that the 1D population synthesis produces net reconnection heating (after self-regulation by gap opening) that exceeds background levels enough to generate observable excess variability and lag deviations. The manuscript states this outcome but supplies no numerical results from the synthesis, such as trapped sBH column, reconnection luminosity relative to coronal heating, or surface-density thresholds at low-to-moderate Eddington ratios, preventing verification that the signatures are actually produced.

    Authors: We agree that the manuscript would be improved by supplying these numerical results to allow verification. In the revised version we will add a dedicated results subsection (or appendix) that reports the outputs of the 1D population synthesis, including trapped sBH column densities at the migration traps, the ratio of reconnection luminosity to background coronal heating across the explored Eddington-ratio range, and the local surface-density thresholds after gap-opening suppression. These quantities will be shown to exceed the levels needed to produce the reported excess short-timescale variability, flattened structure functions, and lag deviations. revision: yes

  2. Referee: [Methods (population synthesis and reprocessing coupling)] The self-regulated heating description is presented as an independent output that produces flattened structure functions and deviations from τ∝λ^{4/3}. Without explicit equations or parameter values for the reconnection rate, the gap-opening suppression factor, or the resulting heating profile in the reprocessing framework, it is unclear whether these deviations are robust predictions or shaped by choices in the population synthesis.

    Authors: We acknowledge that the absence of the explicit equations leaves the robustness of the predictions less transparent. We will expand the methods section to include the full expressions for the reconnection rate (based on local magnetic-field strength and plasma-β), the analytic form of the gap-opening suppression factor, and the procedure by which the localized heating profile is injected into the corona-heated reprocessing variability code. A table of all adopted parameter values will also be added so that readers can reproduce and assess the sensitivity of the structure-function and lag results. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained via model coupling

full rationale

The paper couples a 1D sBH population synthesis model to a corona-heated reprocessing framework and presents the resulting variability signatures (excess short-timescale variability, flattened structure functions, lag deviations) as outputs of that coupling, including the described self-regulation by pile-up enhancement and gap-opening suppression. No equations, fitted parameters renamed as predictions, or load-bearing self-citations are quoted that would make any claimed prediction equivalent to its inputs by construction. The central claim therefore retains independent content from the synthesis-reprocessing link and does not reduce to self-definition or renaming.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard AGN disk assumptions and the migration-trap concept drawn from prior literature; no new free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption Standard thin accretion disk with τ∝λ^{4/3} lag-wavelength relation as baseline
    Invoked when stating deviations produced by the new heating mechanism.
  • domain assumption Stellar-mass black holes migrate efficiently and pile up at migration traps in AGN disks
    Core premise of the 1D population synthesis model.

pith-pipeline@v0.9.1-grok · 5799 in / 1390 out tokens · 33914 ms · 2026-06-26T10:10:22.254098+00:00 · methodology

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Works this paper leans on

95 extracted references · 91 canonical work pages

  1. [1]

    , keywords =

    Abramowicz, M. A., Czerny, B., Lasota, J. P., et al.\ 1988, , 332, 646. doi:10.1086/166683

  2. [2]

    J.\ 2010, Astrophysics of Planet Formation (Cambridge: Cambridge Univ

    Armitage, P. J.\ 2010, Astrophysics of Planet Formation (Cambridge: Cambridge Univ. Press)

  3. [3]

    doi:10.3847/1538-4357/835/2/165

    Bartos, I., Kocsis, B., Haiman, Z., et al.\ 2017, , 835, 2, 165. doi:10.3847/1538-4357/835/2/165

  4. [4]

    M., Mac Low, M.-M., McKernan, B., et al.\ 2016, , 819, 2, L17

    Bellovary, J. M., Mac Low, M.-M., McKernan, B., et al.\ 2016, , 819, 2, L17. doi:10.3847/2041-8205/819/2/L17

  5. [5]

    doi:10.1038/nature14277

    Ben \' tez-Llambay, P., Masset, F., Koenigsberger, G., et al.\ 2015, , 520, 7545, 63. doi:10.1038/nature14277

  6. [6]

    Blandford, R. D. & McKee, C. F.\ 1982, , 255, 419. doi:10.1086/159843

  7. [7]

    MNRAS , author =

    Bondi, H.\ 1952, , 112, 195. doi:10.1093/mnras/112.2.195

  8. [8]

    J., Shen, Y., Blaes, O., et al.\ 2021, Science, 373, 6556, 789

    Burke, C. J., Shen, Y., Blaes, O., et al.\ 2021, Science, 373, 6556, 789. doi:10.1126/science.abg9933

  9. [9]

    M., Bentz, M

    Cackett, E. M., Bentz, M. C., & Kara, E.\ 2021, iScience, 24, 6, 102557. doi:10.1016/j.isci.2021.102557

  10. [10]

    doi:10.3847/1538-4357/aab091

    Cai, Z.-Y., Wang, J.-X., Zhu, F.-F., et al.\ 2018, , 855, 2, 117. doi:10.3847/1538-4357/aab091

  11. [11]

    Cao, Z., Li, Y.-P., Lin, D. N. C., et al.\ 2026, , 999, 1, 55. doi:10.3847/1538-4357/ae4029

  12. [12]

    doi:10.3847/1538-4357/ad16ea

    Chen, J., Sun, M., & Zhang, Z.-X.\ 2024, , 962, 2, 134. doi:10.3847/1538-4357/ad16ea

  13. [13]

    doi:10.3847/1538-4357/acc45f

    Chen, K., Ren, J., & Dai, Z.-G.\ 2023, , 948, 2, 136. doi:10.3847/1538-4357/acc45f

  14. [14]

    doi:10.1093/mnras/stad2269

    Choksi, N., Chiang, E., Fung, J., et al.\ 2023, , 525, 2, 2806. doi:10.1093/mnras/stad2269

  15. [15]

    Collin-Souffrin, S.\ 1991, , 249, 344

  16. [16]

    M., Horne, K., et al.\ 2015, , 806, 1, 129

    Edelson, R., Gelbord, J. M., Horne, K., et al.\ 2015, , 806, 1, 129. doi:10.1088/0004-637X/806/1/129

  17. [17]

    doi:10.1051/0004-6361/202346781

    El Mellah, I., Cerutti, B., & Crinquand, B.\ 2023, , 677, A67. doi:10.1051/0004-6361/202346781

  18. [18]

    doi:10.1093/mnras/staf237

    Epstein-Martin, M., Tagawa, H., Haiman, Z., et al.\ 2025, , 537, 4, 3396. doi:10.1093/mnras/staf237

  19. [19]

    M., Denney, K

    Fausnaugh, M. M., Denney, K. D., Barth, A. J., et al.\ 2016, , 821, 1, 56. doi:10.3847/0004-637X/821/1/56

  20. [20]

    F.\ 2001, , 553, 1, 174

    Gammie, C. F.\ 2001, , 553, 1, 174. doi:10.1086/320631

  21. [21]

    C., Metzger, B

    Generozov, A., Stone, N. C., Metzger, B. D., et al.\ 2018, , 478, 3, 4030. doi:10.1093/mnras/sty1262

  22. [22]

    doi:10.1093/mnras/stt167

    Giannios, D.\ 2013, , 431, 1, 355. doi:10.1093/mnras/stt167

  23. [23]

    & Stone, N

    Gilbaum, S. & Stone, N. C.\ 2022, , 928, 2, 191. doi:10.3847/1538-4357/ac4ded

  24. [24]

    2003, MNRAS, 340, 227, doi: 10.1046/j.1365-8711.2003.06286.x

    Goodman, J.\ 2003, , 339, 4, 937. doi:10.1046/j.1365-8711.2003.06241.x

  25. [25]

    & Cassak, P

    Hesse, M. & Cassak, P. A.\ 2020, Journal of Geophysical Research (Space Physics), 125, 2, e25935. doi:10.1029/2018JA025935

  26. [26]

    Magnetically Driven Accretion Flows in the Kerr Metric

    Hirose, S., Krolik, J. H., De Villiers, J.-P., et al.\ 2004, , 606, 2, 1083. doi:10.1086/383184

  27. [27]

    doi:10.3847/1538-4357/ae2612

    Ida, S., Li, Y.-P., Pan, J.-P., et al.\ 2026, , 997, 2, 160. doi:10.3847/1538-4357/ae2612

  28. [28]

    , keywords =

    Ivanov, P. B., Papaloizou, J. C. B., & Polnarev, A. G.\ 1999, , 307, 1, 79. doi:10.1046/j.1365-8711.1999.02623.x

  29. [29]

    M., et al.\ 2019, , 885, 2, 144

    Jiang, Y.-F., Blaes, O., Stone, J. M., et al.\ 2019, , 885, 2, 144. doi:10.3847/1538-4357/ab4a00

  30. [30]

    S., Dov c iak, M., Papadakis, I

    Kammoun, E. S., Dov c iak, M., Papadakis, I. E., et al.\ 2021, , 907, 1, 20. doi:10.3847/1538-4357/abcb93

  31. [31]

    D., Tanaka, H., Muto, T., et al.\ 2015, , 448, 1, 994

    Kanagawa, K. D., Tanaka, H., Muto, T., et al.\ 2015, , 448, 1, 994. doi:10.1093/mnras/stv025

  32. [32]

    D., Tanaka, H., & Szuszkiewicz, E.\ 2018, , 861, 2, 140

    Kanagawa, K. D., Tanaka, H., & Szuszkiewicz, E.\ 2018, , 861, 2, 140. doi:10.3847/1538-4357/aac8d9

  33. [33]

    Kato, S., Fukue, J., & Mineshige, S.\ 2008, Black-Hole Accretion Disks: Toward a New Paradigm (Kyoto: Kyoto Univ. Press)

  34. [34]

    2004, MNRAS, 350, 1301, doi: 10.1111/j.1365-2966.2004.07713.x

    King, A. R., Pringle, J. E., West, R. G., et al.\ 2004, , 348, 1, 111. doi:10.1111/j.1365-2966.2004.07322.x

  35. [35]

    doi:10.3847/0004-637X/826/2/118

    Koz owski, S.\ 2016, , 826, 2, 118. doi:10.3847/0004-637X/826/2/118

  36. [36]

    H., Horne, K., Kallman, T

    Krolik, J. H., Horne, K., Kallman, T. R., et al.\ 1991, , 371, 541. doi:10.1086/169918

  37. [37]

    doi:10.3847/1538-4357/ad7117

    Laune, J., Li, R., & Lai, D.\ 2024, , 975, 2, 296. doi:10.3847/1538-4357/ad7117

  38. [38]

    doi:10.1038/s41550-017-0372-1

    Lawrence, A.\ 2018, Nature Astronomy, 2, 102. doi:10.1038/s41550-017-0372-1

  39. [39]

    J., et al.\ 2024, , 974, 2, 271

    Lewin, C., Kara, E., Barth, A. J., et al.\ 2024, , 974, 2, 271. doi:10.3847/1538-4357/ad6b08

  40. [40]

    , keywords =

    Levin, Y.\ 2007, , 374, 2, 515. doi:10.1111/j.1365-2966.2006.11155.x

  41. [41]

    , keywords =

    Li, S.-L. & Cao, X.\ 2008, , 387, 1, L41. doi:10.1111/j.1745-3933.2008.00480.x

  42. [42]

    Li, Y.-P., Chen, Y.-X., & Lin, D. N. C.\ 2023, , 526, 4, 5346. doi:10.1093/mnras/stad3049

  43. [43]

    Li, Y.-P., Chen, Y.-X., & Lin, D. N. C.\ 2024, , 971, 2, 130. doi:10.3847/1538-4357/ad5a06

  44. [44]

    Li, Y.-P., Chen, Y.-X., Lin, D. N. C., et al.\ 2021, , 906, 1, 52. doi:10.3847/1538-4357/abc883

  45. [45]

    F., Schekochihin, A

    Loureiro, N. F., Schekochihin, A. A., & Cowley, S. C.\ 2007, Physics of Plasmas, 14, 10, 100703. doi:10.1063/1.2783986

  46. [46]

    E.\ 1997, , 292, 3, 679

    Lyubarskii, Y. E.\ 1997, , 292, 3, 679. doi:10.1093/mnras/292.3.679

  47. [47]

    S.\ 2017, , 472, 4, 4204

    Masset, F. S.\ 2017, , 472, 4, 4204. doi:10.1093/mnras/stx2271

  48. [48]

    M.\ 2013, , 430, L49

    McHardy, I. M.\ 2013, , 430, L49. doi:10.1093/mnrasl/sls048

  49. [49]

    M., Cameron, D

    McHardy, I. M., Cameron, D. T., Dwelly, T., et al.\ 2014, , 444, 2, 1469. doi:10.1093/mnras/stu1636

  50. [50]

    McKernan, B., Ford, K. E. S., Kocsis, B., et al.\ 2014, , 441, 1, 900. doi:10.1093/mnras/stu553

  51. [51]

    McKernan, B., Ford, K. E. S., Lyra, W., et al.\ 2012, , 425, 1, 460. doi:10.1111/j.1365-2966.2012.21486.x

  52. [52]

    McLaughlin, S. A. J., Mullaney, J. R., & Littlefair, S. P.\ 2024, , 529, 3, 2877. doi:10.1093/mnras/stae721

  53. [53]

    Plasmoid formation in global GRMHD simulations and AGN flares[J/OL]

    Nathanail, A., Fromm, C. M., Porth, O., et al.\ 2020, , 495, 2, 1549. doi:10.1093/mnras/staa1165

  54. [54]

    doi:10.1093/mnras/stac1118

    Nathanail, A., Mpisketzis, V., Porth, O., et al.\ 2022, , 513, 3, 4267. doi:10.1093/mnras/stac1118

  55. [55]

    S., Murphy, N

    Ni, L., Lukin, V. S., Murphy, N. A., et al.\ 2018, , 852, 2, 95. doi:10.3847/1538-4357/aa9edb

  56. [56]

    Noble, S. C. & Krolik, J. H.\ 2009, , 703, 1, 964. doi:10.1088/0004-637X/703/1/964

  57. [57]

    J., Kolb, U., & Willems, B

    Paardekooper, S.-J. & Papaloizou, J. C. B.\ 2009, , 394, 4, 2297. doi:10.1111/j.1365-2966.2009.14512.x

  58. [58]

    doi:10.3847/1538-4357/ae261a

    Pan, J., Li, Y.-P., Chen, Y.-X., et al.\ 2026, , 997, 2, 161. doi:10.3847/1538-4357/ae261a

  59. [59]

    B., Hubickyj, O., Bodenheimer, P., et al

    Pollack, J. B., Hubickyj, O., Bodenheimer, P., et al.\ 1996, , 124, 1, 62. doi:10.1006/icar.1996.0190

  60. [60]

    doi:10.3847/1538-4357/ad7b2a

    Ren, G., Zhou, S., Sun, M., et al.\ 2024, , 975, 2, 160. doi:10.3847/1538-4357/ad7b2a

  61. [61]

    , keywords =

    Ripperda, B., Liska, M., Chatterjee, K., et al.\ 2022, , 924, 2, L32. doi:10.3847/2041-8213/ac46a1

  62. [62]

    F., Chernoglazov, A., et al

    Ripperda, B., Mahlmann, J. F., Chernoglazov, A., et al.\ 2021, Journal of Plasma Physics, 87, 5, 905870512. doi:10.1017/S0022377821000957

  63. [63]

    M., Chiang, E

    Rosenthal, M. M., Chiang, E. I., Ginzburg, S., et al.\ 2020, , 498, 2, 2054. doi:10.1093/mnras/staa1721

  64. [64]

    doi:10.3847/1538-4357/ab20ca

    Secunda, A., Bellovary, J., Mac Low, M.-M., et al.\ 2019, , 878, 2, 85. doi:10.3847/1538-4357/ab20ca

  65. [65]

    Shakura, N. I. & Sunyaev, R. A.\ 1973, , 24, 337

  66. [66]

    T., Strauss, M

    Shen, Y., Richards, G. T., Strauss, M. A., et al.\ 2011, , 194, 2, 45. doi:10.1088/0067-0049/194/2/45

  67. [67]

    & Begelman, M

    Sikora, M. & Begelman, M. C.\ 2013, , 764, 2, L24. doi:10.1088/2041-8205/764/2/L24

  68. [68]

    2003, MNRAS, 340, 227, doi: 10.1046/j.1365-8711.2003.06286.x

    Sirko, E. & Goodman, J.\ 2003, , 341, 2, 501. doi:10.1046/j.1365-8711.2003.06431.x

  69. [69]

    C.\ 2025, , 695, A268

    Son, S., Kim, M., & Ho, L. C.\ 2025, , 695, A268. doi:10.1051/0004-6361/202452467

  70. [70]

    C., Metzger, B

    Stone, N. C., Metzger, B. D., & Haiman, Z.\ 2017, , 464, 1, 946. doi:10.1093/mnras/stw2260

  71. [71]

    doi:10.3847/1538-4357/adef13

    Su, Z.-B., Cai, Z.-Y., Guo, H., et al.\ 2025, , 990, 1, 10. doi:10.3847/1538-4357/adef13

  72. [72]

    N., et al.\ 2020a, , 891, 2, 178

    Sun, M., Xue, Y., Brandt, W. N., et al.\ 2020a, , 891, 2, 178. doi:10.3847/1538-4357/ab789e

  73. [73]

    doi:10.3847/1538-4357/abb1c4

    Sun, M., Xue, Y., Guo, H., et al.\ 2020b, , 902, 1, 7. doi:10.3847/1538-4357/abb1c4

  74. [74]

    , keywords =

    Tagawa, H., Haiman, Z., & Kocsis, B.\ 2020, , 898, 1, 25. doi:10.3847/1538-4357/ab9b8c

  75. [75]

    S., Haiman, Z., et al.\ 2022, , 927, 1, 41

    Tagawa, H., Kimura, S. S., Haiman, Z., et al.\ 2022, , 927, 1, 41. doi:10.3847/1538-4357/ac45f8

  76. [76]

    S., Haiman, Z., et al.\ 2023, , 946, 1, L3

    Tagawa, H., Kimura, S. S., Haiman, Z., et al.\ 2023, , 946, 1, L3. doi:10.3847/2041-8213/acc103

  77. [77]

    doi:10.3847/1538-4357/ab77af

    Tanaka, H., Murase, K., & Tanigawa, T.\ 2020, , 891, 2, 143. doi:10.3847/1538-4357/ab77af

  78. [78]

    R.\ 2002, , 565, 2, 1257

    Tanaka, H., Takeuchi, T., & Ward, W. R.\ 2002, , 565, 2, 1257. doi:10.1086/324713

  79. [79]

    & Tanaka, H.\ 2016, , 823, 1, 48

    Tanigawa, T. & Tanaka, H.\ 2016, , 823, 1, 48. doi:10.3847/0004-637X/823/1/48

  80. [80]

    Tan, A. H. T., Wolf, C., Amrutha, N., et al.\ 2026, . doi:10.1093/mnras/stag1052

Showing first 80 references.