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arxiv: 2603.11169 · v1 · submitted 2026-03-11 · 🌌 astro-ph.GA · astro-ph.CO

Recognition: no theorem link

Large-scale environments of star-forming active galactic nuclei: How black hole mass, accretion rate, and luminosity connect to dark matter halos

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Pith reviewed 2026-05-15 13:12 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords active galactic nucleidark matter halosblack hole massEddington ratioX-ray luminositygalaxy environment
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The pith

X-ray AGN typically reside in dark matter halos of mass about 10^13 solar masses with no significant dependence on black hole mass, Eddington ratio or X-ray luminosity.

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

This paper investigates whether the large-scale environments of active galactic nuclei depend on their black hole properties. By combining X-ray surveys and using cross-correlations with galaxies plus a matching technique to control for host properties, the authors find AGN in halos of similar mass irrespective of those properties. This points to internal galaxy processes as the main regulators of AGN activity rather than external environment. Readers should care as it refines our understanding of what drives black hole growth in galaxies over cosmic time.

Core claim

Within the uncertainties of the present dataset, X-ray AGN typically reside in halos of log(M_DMH/h^{-1}M_⊙)≃13, with no significant variation as a function of M_BH, λ_Edd, or L_X. These results suggest that neither long-term black-hole growth nor short-term accretion variability is strongly linked to large-scale environment, and instead support a scenario in which AGN properties are regulated primarily by internal host-galaxy processes, while large-scale structure sets the broader boundary conditions for gas supply and duty cycle.

What carries the argument

Multivariate nearest-neighbour matching algorithm to control for host-galaxy properties while measuring AGN-galaxy cross-correlation functions to infer dark matter halo masses.

If this is right

  • Black hole growth occurs independently of the surrounding dark matter halo mass.
  • Short-term changes in accretion rate are not tied to large-scale structure.
  • Internal processes within the host galaxy primarily control AGN properties.
  • Large-scale environment provides only boundary conditions for gas availability.

Where Pith is reading between the lines

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

  • If the result holds, galaxy evolution models can prioritize internal feedback over environmental triggers for AGN.
  • Extending the analysis to higher redshifts could reveal if this independence evolves with cosmic time.
  • Future surveys with better halo mass estimates might detect subtle dependencies not seen here.

Load-bearing premise

The multivariate nearest-neighbour matching algorithm fully isolates trends with black-hole mass, Eddington ratio and X-ray luminosity by controlling for host-galaxy properties without residual biases from unaccounted variables or selection effects.

What would settle it

Detection of a statistically significant difference in halo mass as a function of black hole mass or Eddington ratio in a larger dataset using the same methodology would falsify the no-variation claim.

Figures

Figures reproduced from arXiv: 2603.11169 by A. Georgakakis, F. J. Carrera, F. Shankar, G. Mountrichas.

Figure 1
Figure 1. Figure 1: Distributions of the sSFR (sSFR = SFR/M⋆), for galaxies (top panel) and X-ray AGN (bottom panel) for the two fields, as indicated in the legend. component in their SEDs. We removed all galaxies with an AGN fractional contribution fracAGN > 0.2 (e.g. Mountrichas et al. 2021b, 2022c,a,b, 2024b), where fracAGN denotes the fraction of the total infrared emission attributed to AGN heating. This criterion exclud… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of key physical properties for the combined X-ray AGN sample from the XXL and Stripe 82X surveys. From [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Correlation function measurements: Top panel: [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: AGN–galaxy cross-correlation functions as a function of [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: AGN–galaxy cross-correlation functions as a function of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Dependence of the characteristic DMH mass on the [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original abstract

Understanding the relative roles of large-scale environment and internal host-galaxy processes in shaping AGN activity is essential for constraining models of black-hole growth and galaxy evolution. We investigate how the environment of X-ray selected active galactic nuclei (AGN) relates to black-hole growth and accretion properties, and whether these introduce an environmental dependence beyond that expected from the host galaxy itself. Combining the XXL and Stripe 82X surveys, we construct samples of 427 broad-line AGN at $0.5<z<1.2$ and more than $20,000$ galaxies, with host-galaxy properties derived consistently using the same spectral energy distribution fitting methodology. Dark matter halo (DMH) masses are inferred from AGN--galaxy cross-correlation functions, while a multivariate nearest-neighbour matching algorithm is used to isolate trends with black-hole mass ($M_{\mathrm{BH}}$), Eddington ratio ($\lambda_{\mathrm{Edd}}$), and X-ray luminosity ($L_{\mathrm{X}}$) under controlled host-galaxy conditions. Within the uncertainties of the present dataset, X-ray AGN typically reside in halos of $\log(M_{\mathrm{DMH}}/h^{-1}M_\odot)\simeq13$, with no significant variation as a function of $M_{\mathrm{BH}}$, $\lambda_{\mathrm{Edd}}$, or $L_{\mathrm{X}}$. These results suggest that neither long-term black-hole growth nor short-term accretion variability is strongly linked to large-scale environment, and instead support a scenario in which AGN properties are regulated primarily by internal host-galaxy processes, while large-scale structure sets the broader boundary conditions for gas supply and duty cycle.

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 paper analyzes X-ray selected AGN at 0.5<z<1.2 from the XXL and Stripe 82X surveys, using AGN-galaxy cross-correlation functions to infer dark matter halo masses and a multivariate nearest-neighbour matching algorithm to control for host-galaxy properties derived via consistent SED fitting. It reports that AGN typically occupy halos with log(M_DMH/h^{-1}M_⊙)≃13 and finds no significant variation in halo mass as a function of black-hole mass, Eddington ratio, or X-ray luminosity, concluding that AGN properties are regulated primarily by internal host-galaxy processes rather than large-scale environment.

Significance. If the null result on environmental dependence holds after validation, the work would provide a useful observational constraint on AGN fueling models, reinforcing the dominance of internal processes in black-hole growth while large-scale structure sets only broad boundary conditions. The combined survey sample (>20,000 galaxies) and uniform SED methodology strengthen its potential utility for comparisons with simulations of galaxy evolution.

major comments (2)
  1. [Methods (matching algorithm)] The multivariate nearest-neighbour matching algorithm (described in the methods) is load-bearing for the central claim of no residual environmental trends after controlling for host properties. No quantitative post-matching diagnostics are reported, such as residual correlations between AGN parameters and unmatched variables (e.g., morphology or local density proxies) or bootstrap tests varying neighbour count and distance metric. This leaves open the possibility that unaccounted biases mimic the reported independence.
  2. [Abstract and §4] Abstract and §4 (results): The claim of 'no significant variation within uncertainties' is presented without the full error budget, including contributions from cosmic variance, sample selection, and matching uncertainties. This creates a verification gap for the null result on halo-mass independence with M_BH, λ_Edd, and L_X.
minor comments (2)
  1. [Methods] Clarify the exact host-galaxy variables included in the matching (stellar mass, SFR, etc.) and any weighting scheme in the distance metric for reproducibility.
  2. [Abstract] The halo-mass notation log(M_DMH/h^{-1}M_⊙) in the abstract should be defined on first use with explicit reference to the cosmology assumed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major point below and will revise the paper accordingly to improve the robustness and transparency of our analysis.

read point-by-point responses
  1. Referee: [Methods (matching algorithm)] The multivariate nearest-neighbour matching algorithm (described in the methods) is load-bearing for the central claim of no residual environmental trends after controlling for host properties. No quantitative post-matching diagnostics are reported, such as residual correlations between AGN parameters and unmatched variables (e.g., morphology or local density proxies) or bootstrap tests varying neighbour count and distance metric. This leaves open the possibility that unaccounted biases mimic the reported independence.

    Authors: We agree that additional quantitative diagnostics are needed to fully validate the matching procedure. In the revised manuscript we will add an appendix with post-matching checks, including Kolmogorov-Smirnov tests on the distributions of stellar mass, SFR and redshift before and after matching, assessment of residual correlations with unmatched variables (morphology from imaging where available, and local density proxies), and stability tests by varying neighbour count (5, 10, 20) and distance metric (Euclidean vs. Mahalanobis). These will demonstrate that the reported independence is not driven by residual biases. revision: yes

  2. Referee: [Abstract and §4] Abstract and §4 (results): The claim of 'no significant variation within uncertainties' is presented without the full error budget, including contributions from cosmic variance, sample selection, and matching uncertainties. This creates a verification gap for the null result on halo-mass independence with M_BH, λ_Edd, and L_X.

    Authors: We acknowledge that a more complete error budget is required for the null-result claims. In the revision we will expand §4 with an explicit breakdown of uncertainties: cosmic variance estimated via jackknife resampling over the XXL and Stripe 82X fields, sample-selection effects tested with mock catalogs, and matching uncertainties quantified through bootstrap resampling of the neighbour assignments. These contributions will be tabulated alongside the correlation-function statistical errors and referenced in the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity; result is direct observational measurement

full rationale

The paper derives DMH masses from AGN-galaxy cross-correlation functions and applies a data-driven multivariate nearest-neighbour matching procedure to control for host-galaxy properties. Neither step reduces by the paper's own equations to a quantity defined in terms of a fitted parameter or prior self-citation. The null result (no variation of log M_DMH with M_BH, λ_Edd or L_X) follows from the measured correlation lengths after matching; it is not forced by construction. The analysis is self-contained against external benchmarks and contains no load-bearing self-citation chains or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the standard conversion of AGN-galaxy cross-correlation functions into halo masses under Lambda-CDM and on the assumption that nearest-neighbour matching removes all host-galaxy confounding effects.

axioms (1)
  • standard math Lambda-CDM cosmology is used to convert measured correlation lengths into dark matter halo masses
    Invoked in the cross-correlation analysis described in the abstract.

pith-pipeline@v0.9.0 · 5625 in / 1389 out tokens · 64661 ms · 2026-05-15T13:12:42.525791+00:00 · methodology

discussion (0)

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Works this paper leans on

102 extracted references · 102 canonical work pages · 1 internal anchor

  1. [1]

    L., Moustakas, J., et al

    Aird, J., Coil, A. L., Moustakas, J., et al. 2012, ApJ, 746, 90

  2. [2]

    2021, AJ, 914, 7

    Allevato, V ., Miyaji, T., Georgakakis, A., et al. 2021, AJ, 914, 7

  3. [3]

    2019, A&A, 632, A88

    Allevato, V ., Viitanen, A., Finoguenov, A., et al. 2019, A&A, 632, A88

  4. [4]

    Allevato, V . et al. 2011, ApJ, 736, 99

  5. [5]

    Allevato, V . et al. 2014, ApJ, 796, 4

  6. [6]

    T., Salvato, M., LaMassa, S., et al

    Ananna, T. T., Salvato, M., LaMassa, S., et al. 2017, ApJ, 850, 66

  7. [7]

    2023, in American Astronomical Society Meeting Abstracts, V ol

    Anderson, S., Eracelous, M., Green, P., et al. 2023, in American Astronomical Society Meeting Abstracts, V ol. 241, American Astronomical Society Meet- ing Abstracts #241, 301.03

  8. [8]

    A., et al

    Annis, J., Soares-Santos, M., Strauss, M. A., et al. 2014, ApJ, 794, 120

  9. [9]

    2025, A&A, 698, A132

    Aydar, C., Merloni, A., Dwelly, T., et al. 2025, A&A, 698, A132

  10. [10]

    Booth, C. M. & Schaye, J. 2010, MNRAS, 405, L1

  11. [11]

    G., Benson, A

    Bower, R. G., Benson, A. J., Malbon, R., et al. 2006, MNRAS, 370, 645

  12. [12]

    G., Schaye, J., Furlong, M., et al

    Bower, R. G., Schaye, J., Furlong, M., et al. 2017, MNRAS, 465, 32

  13. [13]

    Brandt, W. N. & Hasinger, G. 2005, Annual Review of Astronomy and Astro- physics, 43, 827

  14. [14]

    & Charlot, S

    Bruzual, G. & Charlot, S. 2003, MNRAS, 344, 1000

  15. [15]

    2019, A&A, 632, A79

    Buat, V ., Ciesla, L., Boquien, M., Małek, K., & Burgarella, D. 2019, A&A, 632, A79

  16. [16]

    2021, A&A, 654, A93

    Buat, V ., Mountrichas, G., Yang, G., et al. 2021, A&A, 654, A93

  17. [17]

    & Fall, S

    Charlot, S. & Fall, S. M. 2000, ApJ, 539, 718

  18. [18]

    L., Mendez, A

    Coil, A. L., Mendez, A. J., Eisenstein, D. J., & Moustakas, J. 2017, ApJ, 838, 87

  19. [19]

    Croton, D. J. et al. 2006, MNRAS, 365, 11

  20. [20]

    A., Helou, G., Magdis, G

    Dale, D. A., Helou, G., Magdis, G. E., et al. 2014, ApJ, 784, 83

  21. [21]

    The DESI Experiment Part I: Science,Targeting, and Survey Design

    Davis, M. & Peebles, P. J. E. 1983, ApJ, 267, 465 de Jong, R. S., Bellido-Tirado, O., Chiappini, C., et al. 2012, in Ground-based and Airborne Instrumentation for Astronomy IV , ed. I. S. McLean, S. K. Ram- say, & H. Takami, V ol. 8446 (SPIE), 84460T DESI Collaboration, Aghamousa, A., Aguilar, J., & et al. 2016, arXiv e-prints [arXiv:1611.00036]

  22. [22]

    N., & Heckman, T

    Donoso, E., Li, C., Kauffmann, G., Best, P. N., & Heckman, T. M. 2010, MN- RAS, 407, 1078

  23. [23]

    2012, MNRAS, 420, 2662

    Dubois, Y ., Devriendt, J., Slyz, A., & Teyssier, R. 2012, MNRAS, 420, 2662

  24. [24]

    Fanidakis, N. et al. 2012, MNRAS, 419, 2797

  25. [25]

    Garilli, B. et al. 2014, A&A, 562, 23

  26. [26]

    2019, MNRAS, 487, 275

    Georgakakis, A., Comparat, J., Merloni, A., et al. 2019, MNRAS, 487, 275

  27. [27]

    Georgakakis, A. et al. 2011, MNRAS, 418, 2590

  28. [28]

    Georgakakis, A. et al. 2014, MNRAS, 443, 3327

  29. [29]

    Georgakakis, A. et al. 2017, MNRAS, 469, 3232

  30. [30]

    Guzzo, L. et al. 2014, A&A, 566, 108

  31. [31]

    2022, MNRAS, 511, 3751

    Habouzit, M., Onoue, M., Bañados, E., et al. 2022, MNRAS, 511, 3751

  32. [32]

    Hickox, R. C. et al. 2009, ApJ, 696, 891

  33. [33]

    2014, MNRAS, 442, 2304

    Hirschmann, M., Dolag, K., Saro, A., et al. 2014, MNRAS, 442, 2304

  34. [34]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., & Kereš, D. 2008, AJSS, 175, 356

  35. [35]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2006, ApJS, 163, 1

  36. [36]

    2025, ApJ, 982, 192

    Ikeda, H., Miyaji, T., Ueda, Y ., et al. 2025, ApJ, 982, 192

  37. [37]

    2014, ApJS, 213, 12

    Jiang, L., Fan, X., Bian, F., et al. 2014, ApJS, 213, 12

  38. [38]

    2022, A&A, 658, A35

    Koutoulidis, L., Mountrichas, G., Georgantopoulos, I., Pouliasis, E., & Plionis, M. 2022, A&A, 658, A35

  39. [39]

    2013, MN- RAS, 428, 1382

    Koutoulidis, L., Plionis, M., Georgantopoulos, I., & Fanidakis, N. 2013, MN- RAS, 428, 1382

  40. [40]

    L., & Aceves, H

    Krumpe, M., Miyaji, T., Coil, A. L., & Aceves, H. 2012, ApJ, 746, 1

  41. [41]

    L., & Hector, A

    Krumpe, M., Miyaji, T., Coil, A. L., & Hector, A. 2018, MNRAS, 474, 1773

  42. [42]

    2023, ApJ, 952, 109

    Krumpe, M., Miyaji, T., Georgakakis, A., et al. 2023, ApJ, 952, 109

  43. [43]

    2015, ApJ, 815, 21

    Krumpe, M., Miyaji, T., Husemann, B., et al. 2015, ApJ, 815, 21

  44. [44]

    M., et al

    LaMassa, S., Peca, A., Urry, C. M., et al. 2024, ApJ, 974, 235

  45. [45]

    M., Cales, S., Moran, E

    LaMassa, S. M., Cales, S., Moran, E. C., et al. 2015, ApJ, 800, 144

  46. [46]

    M., Georgakakis, A., Vivek, M., et al

    LaMassa, S. M., Georgakakis, A., Vivek, M., et al. 2019, ApJ, 876, 50

  47. [47]

    LaMassa, S. M. et al. 2016, ApJ, 817, 21

  48. [48]

    Landy, S. D. & Szalay, A. S. 1993, ApJ, 412, 64

  49. [49]

    Lawrence, A. et al. 2007, MNRAS, 379, 1599 Le Fèvre, O. et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood, 1670–1681

  50. [50]

    Leauthaud, A. et al. 2015, MNRAS, 446, 1874

  51. [51]

    2016, MNRAS, 459, 1602 Małek, K., Buat, V ., Roehlly, Y ., et al

    Liu, Z., Merloni, A., Georgakakis, A., et al. 2016, MNRAS, 459, 1602 Małek, K., Buat, V ., Roehlly, Y ., et al. 2018, A&A, 620, A50

  52. [52]

    A., Mountrichas, G., Georgantopoulos, I., & Plionis, M

    Masoura, V . A., Mountrichas, G., Georgantopoulos, I., & Plionis, M. 2021, A&A, 646, A167

  53. [53]

    A., Mountrichas, G., Georgantopoulos, I., et al

    Masoura, V . A., Mountrichas, G., Georgantopoulos, I., et al. 2018, A&A, 618, 31

  54. [54]

    2023, A&A, 674, A181

    Menci, N., Fiore, F., Shankar, F., Zanisi, L., & Feruglio, C. 2023, A&A, 674, A181

  55. [55]

    Mendez, A. J. et al. 2016, ApJ, 821, 55

  56. [56]

    Menzel, M.-L. et al. 2016, MNRAS, 457, 110

  57. [57]

    2024, Astronomy & Astrophysics, 682, A34

    Merloni, A., Liu, T., Predehl, P., & et al. 2024, Astronomy & Astrophysics, 682, A34

  58. [58]

    L., & Aceves, H

    Miyaji, T., Krumpe, M., Coil, A. L., & Aceves, H. 2011, ApJ, 726, 83

  59. [59]

    2023, A&A, 672, A98

    Mountrichas, G. 2023, A&A, 672, A98

  60. [60]

    & Buat, V

    Mountrichas, G. & Buat, V . 2023, A&A, 679, A151

  61. [61]

    J., Georgantopoulos, I., et al

    Mountrichas, G., Carrera, F. J., Georgantopoulos, I., et al. 2025, A&A, 700, A234

  62. [62]

    & Georgakakis, A

    Mountrichas, G. & Georgakakis, A. 2012, MNRAS, 420, 514

  63. [63]

    2019, MNRAS, 483, 1374

    Mountrichas, G., Georgakakis, A., & Georgantopoulos, I. 2019, MNRAS, 483, 1374

  64. [64]

    & Georgantopoulos, I

    Mountrichas, G. & Georgantopoulos, I. 2024, A&A, 683, A160

  65. [65]

    2009, MNRAS, 398, 971

    Mountrichas, G., Sawangwit, U., & Shanks, T. 2009, MNRAS, 398, 971

  66. [66]

    2009, MNRAS, 394, 2050

    Mountrichas, G., Sawangwit, U., Shanks, T., et al. 2009, MNRAS, 394, 2050

  67. [67]

    & Shankar, F

    Mountrichas, G. & Shankar, F. 2023, MNRAS, 518, 2088

  68. [68]

    2023, A&A, 675, A137

    Mountrichas, G., Yang, G., Buat, V ., et al. 2023, A&A, 675, A137

  69. [69]

    Mountrichas, G. et al. 2013, MNRAS, 430, 661

  70. [70]

    Mountrichas, G. et al. 2016, MNRAS, 457, 4195

  71. [71]

    M., et al

    Peca, A., Cappelluti, N., Urry, C. M., et al. 2023, ApJ, 943, 162

  72. [72]

    Pierre, M. et al. 2016, A&A, 592, 1 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6

  73. [73]

    2022, A&A, 667, A56

    Pouliasis, E., Mountrichas, G., Georgantopoulos, I., et al. 2022, A&A, 667, A56

  74. [74]

    2020, MNRAS, 495, 1853

    Pouliasis, E., Mountrichas, G., Georgantopoulos, I., et al. 2020, MNRAS, 495, 1853

  75. [75]

    C., Cappelluti, N., Urry, C

    Powell, M. C., Cappelluti, N., Urry, C. M., et al. 2018, ApJ, 858, 110

  76. [76]

    C., Urry, C

    Powell, M. C., Urry, C. M., Cappelluti, N., et al. 2020, ApJ, 891, 41

  77. [77]

    P., Shanks, T., Cannon, R

    Ross, N. P., Shanks, T., Cannon, R. D., et al. 2008, MNRAS, 387, 1323

  78. [78]

    2018, A&A, 609, A84

    Scodeggio, M., Guzzo, L., Garilli, B., et al. 2018, A&A, 609, A84

  79. [79]

    2020, Nature Astronomy, 4, 282

    Shankar, F., Allevato, V ., Bernardi, M., et al. 2020, Nature Astronomy, 4, 282

  80. [80]

    2025, MNRAS, 530, 2174

    Shankar, F., Bernardi, M., Roberts, D., et al. 2025, MNRAS, 530, 2174

Showing first 80 references.