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arxiv: 2606.14643 · v2 · pith:MSPAR4KTnew · submitted 2026-06-12 · 🌌 astro-ph.GA

A new CIGALE module for modeling AGN emission lines

Hao Zhang , Patrice Theule , Veronique Buat , Denis Burgarella , Estelle Pons , Mederic Boquien This is my paper

Pith reviewed 2026-06-27 04:27 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords AGN emission linesphotoionization modelingSED fittingbroad line regionnarrow line regionCIGALECloudymetallicity
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The pith

A new CIGALE module uses Cloudy to model AGN broad and narrow line region emission from X-ray and accretion disk continua.

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

The paper introduces the nebular_AGN module for the SED-fitting code CIGALE to simulate and fit emission lines from the broad-line and narrow-line regions of active galactic nuclei. It builds this module by feeding CIGALE's existing X-ray and accretion-disk radiation fields into the Cloudy photoionization code, then varying gas-cloud parameters within the code's AGN geometry and dust framework to create a usable database. The work tests the module against a quasar composite spectrum, empirical metallicity relations, and X-ray-selected AGN observations. A sympathetic reader would care because rising numbers of high-redshift AGNs require practical tools that extract physical properties directly from emission-line data during routine SED fitting.

Core claim

The nebular_AGN module, built from Cloudy models driven by CIGALE's X-ray and accretion-disk continua together with its AGN geometry and dust settings, approximately reproduces the majority of quasar emission-line profiles, spans the key emission-line ratios seen in AGN samples, and returns estimates of the physical properties of the line-emitting gas. For certain parameter combinations the module's derived metallicity matches the empirical calibration formula.

What carries the argument

The nebular_AGN module, a pre-computed database of emission-line spectra generated by Cloudy photoionization calculations on gas clouds whose incident radiation fields come from CIGALE's X-ray and accretion-disk continua.

If this is right

  • Users can now derive BLR and NLR physical properties such as metallicity directly inside CIGALE SED fits of AGN spectra.
  • The module covers the main observed emission-line ratios, allowing consistent modeling of both continuum and lines in a single fit.
  • A line-sensitivity analysis within the module identifies which diagnostic lines respond most strongly to each input parameter.
  • The dust attenuation law remains a dominant factor in the overall SED fit even after the new line module is included.

Where Pith is reading between the lines

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

  • The same Cloudy-based approach could be adapted to other SED codes that already contain X-ray and disk continua.
  • Running the module on large spectroscopic surveys would test whether the parameter grid is dense enough to avoid interpolation biases.
  • Discrepancies between module metallicities and empirical calibrations at extreme parameter values could point to missing physics such as non-solar abundance patterns.

Load-bearing premise

The AGN geometry and dust settings already implemented in CIGALE, together with the X-ray and accretion-disk continua, produce incident radiation fields that are sufficiently realistic for the Cloudy models to match real AGN observations.

What would settle it

A systematic mismatch between the module's predicted line ratios and the observed ratios in the same X-ray-selected AGN sample used for benchmarking would falsify the claim that the generated database adequately represents real AGN emission.

Figures

Figures reproduced from arXiv: 2606.14643 by Denis Burgarella, Estelle Pons, Hao Zhang, Mederic Boquien, Patrice Theule, Veronique Buat.

Figure 1
Figure 1. Figure 1: Sketch of the AGN geometrical structure used in the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The incident radiation field used in the radiative transfer [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spectral shape of the incident radiation field for di [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The comparison between the composite quasar spectrum (blue solid line) and the best-fit spectrum (red dashed line) is shown [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: We fix alpha_ox = −1.1 and nH_NLR = 3.0, and explore the agreement between the CIGALE metallicity and the Dors (2021) metallicity under different combinations of gam and logU_NLR, as well as the corresponding trends of R23 and P. 0.00 0.01 0.02 0.03 0.04 0.05 Dors metallicity OX = -1.9 lognH = 2 lognH = 3 lognH = 4 0.00 0.01 0.02 0.03 0.04 0.05 Dors metallicity OX = -1.5 0.00 0.01 0.02 0.03 0.04 0.05 Dors … view at source ↗
Figure 6
Figure 6. Figure 6: We fix gam = 3.0 and logU_NLR = 2.3 , and explore the agreement between the CIGALE metallicity and the Dors (2021) metallicity under different combinations of alpha_ox and nH_NLR, as well as the corresponding trends of R23 and P. gen density nH_NLR over 2.0, 3.0, and 4.0; and the ionization pa￾rameter of the NLR over logU_NLR = -3.5, -2.9, -2.3, and -1.8. For the covering factors, f_NLR is fixed to 0.1 and… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison between observations and CIGALE simulations on three commonly used BPT/ [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of observed line fluxes with Bayesian-like estimated fluxes of CIGALE, without and with [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: We use black lines with different styles to represent the incident radiation fields with different Γ values adopted in the [nebular_AGN] module. Their luminosities are all scaled to the same value as in Feltre et al. (2016), i.e., LAGN = 1045 erg s−1 . The grey shaded area indicates the accretion disk spectra used as incident radiation fields in the models of Feltre et al. (2016), with power-law indices be… view at source ↗
Figure 11
Figure 11. Figure 11: The UV diagnostic grids built using the [nebular_AGN] module, with nH = 103 cm−3 , logU = -1, -2, -3, -4 and Γ = 1.8, 2.4, 3.0, 3.6, compared to [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: The BPT/VO87 diagnostic grids constructed by [nebular_AGN] module with different ionization parameters and photon indices, compared to Figs. 2, 3, and 4 of Feltre et al. (2016). number of ionizing photons, followed by Γ = 3.0, Γ = 1.8, and Γ = 4.2, which yields the lowest. In the 25–50 eV range, the ordering changes to Γ = 3.0 being the most dominant, followed by Γ = 2.4, Γ = 4.2, and Γ = 1.8. In contrast… view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of the distributions of observed NLR emis [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
read the original abstract

Aims. The increasing discovery of high-redshift AGNs in recent years imposes more stringent requirements on spectral analysis tools for deriving the properties of AGNs and their host galaxies from emission-line diagnostics. To address this need, we develop a new module for the popular SED-fitting tool Code Investigating GALaxy Emission (CIGALE), the [nebular_AGN] module, which enables the efficient and flexible simulation and fitting of emission lines originating from the broad-line regions (BLRs) and narrow-line regions (NLRs) of AGNs, and allows the estimation of the physical properties of these regions. Methods. We use the spectral synthesis code Cloudy to construct the database for the new module. Based on the X-ray and accretion disk continua implemented in CIGALE, we generate the incident radiation fields of the models. We then adopt the AGN geometry and dust settings implemented in CIGALE to define a flexible set of physical parameters for the gas clouds, thereby producing a comprehensive database for the [nebular_AGN] module. Results. We benchmark the [nebular_AGN] module using a quasar composite spectrum, an empirical metallicity calibration, and observational data from X-ray-selected AGNs. Our module can approximately reproduce the majority of quasar emission-line profiles, cover the key emission-line ratios observed in AGN samples, and provide an assessment of their physical properties. For specific combinations of parameters, the metallicity derived by our module is consistent with the empirical formula. We further compare our models with other photoionization models used to simulate AGN NLR emission, and perform a line-sensitivity study to identify the most effective diagnostic lines for each parameter in our module. Finally, we confirm that the dust attenuation law plays an important role in SED fitting.

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 introduces the [nebular_AGN] module for CIGALE. The module builds a Cloudy grid of BLR and NLR emission lines whose incident radiation fields are taken from CIGALE's existing X-ray and accretion-disk continua; gas-cloud parameters are set using CIGALE's pre-existing AGN geometry and dust prescriptions. The module is benchmarked against a quasar composite spectrum, an empirical metallicity calibration, and X-ray-selected AGN data, with claims that it approximately reproduces the majority of quasar line profiles, covers observed line ratios, and yields metallicities consistent with the empirical relation for some parameter combinations. A line-sensitivity analysis and comparison to other NLR photoionization models are also presented.

Significance. If the adopted incident continua are sufficiently realistic, the module would supply a practical, integrated tool for simultaneous SED and emission-line fitting of AGN hosts inside the widely used CIGALE framework, supporting physical-property estimates for high-redshift samples. The line-sensitivity study and explicit comparison to other Cloudy-based NLR grids constitute concrete strengths that aid users in choosing diagnostics.

major comments (2)
  1. [Methods] Methods section: The incident radiation fields are generated directly from CIGALE's pre-existing X-ray and accretion-disk continua without any quantitative comparison to observed mean quasar SEDs (e.g., Elvis et al. or Richards et al. composites) or to the ionizing-photon distributions employed in standard AGN photoionization grids. This assumption is load-bearing for the central claim that the module can 'assess physical properties' and match real AGN samples.
  2. [Results] Results section: Benchmarking against the quasar composite is described only qualitatively ('approximately reproduce'). No quantitative metrics (reduced χ², fractional residuals on key ratios such as [O III]/Hβ or C IV/Lyα) are reported, leaving open the possibility that systematic offsets remain undetected.
minor comments (2)
  1. [Results] The abstract states that 'the dust attenuation law plays an important role,' but the quantitative impact on derived parameters is not shown in a dedicated figure or table.
  2. Sample selection criteria, redshift range, and number of objects for the X-ray-selected AGN comparison should be stated explicitly rather than referenced only at abstract level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and will incorporate revisions to strengthen the presentation of the methods and results.

read point-by-point responses

  1. Referee: [Methods] Methods section: The incident radiation fields are generated directly from CIGALE's pre-existing X-ray and accretion-disk continua without any quantitative comparison to observed mean quasar SEDs (e.g., Elvis et al. or Richards et al. composites) or to the ionizing-photon distributions employed in standard AGN photoionization grids. This assumption is load-bearing for the central claim that the module can 'assess physical properties' and match real AGN samples.

    Authors: The nebular_AGN module is designed for direct integration with CIGALE, so the incident continua are taken from the same X-ray and accretion-disk prescriptions already implemented in the code to ensure consistency between continuum and line modeling. We acknowledge that an explicit quantitative comparison to observed quasar SED composites (e.g., Elvis et al., Richards et al.) and to the ionizing spectra used in other standard photoionization grids would provide useful context. We will add this comparison, including a description of the ionizing photon distributions, to the revised Methods section. revision: yes

  2. Referee: [Results] Results section: Benchmarking against the quasar composite is described only qualitatively ('approximately reproduce'). No quantitative metrics (reduced χ², fractional residuals on key ratios such as [O III]/Hβ or C IV/Lyα) are reported, leaving open the possibility that systematic offsets remain undetected.

    Authors: We agree that the current description of the quasar composite benchmark is qualitative. In the revised Results section we will add quantitative metrics, specifically reduced χ² for the overall fit to the composite spectrum and fractional residuals (or equivalent percentage differences) for key line ratios including [O III]/Hβ and C IV/Lyα. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new module grids validated externally

full rationale

The paper builds the [nebular_AGN] module by feeding CIGALE's existing X-ray/accretion-disk continua and AGN geometry/dust settings into Cloudy to generate a new parameter grid. Central results (reproduction of quasar composite profiles, coverage of observed line ratios, and partial match to empirical metallicity) are obtained by direct comparison to independent external datasets (quasar composites, X-ray-selected AGN observations, and an empirical calibration formula) rather than by fitting parameters inside the paper or reducing to self-citations. No self-definitional, fitted-input-renamed-as-prediction, or load-bearing self-citation steps appear in the derivation chain.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard photoionization assumptions plus the pre-existing CIGALE continua and geometry choices; no new entities are postulated.

free parameters (1)
  • gas cloud physical parameters
    Density, metallicity, ionization parameter, covering factor and other cloud properties are varied to populate the model grid.
axioms (2)
  • domain assumption X-ray and accretion-disk continua already present in CIGALE constitute appropriate incident radiation fields for AGN NLR and BLR modeling.
    Invoked to generate the incident radiation fields for Cloudy.
  • domain assumption AGN geometry and dust settings already implemented in CIGALE are flexible enough to represent real BLR and NLR conditions.
    Used to define the parameter space of the gas clouds.

pith-pipeline@v0.9.1-grok · 5862 in / 1322 out tokens · 35493 ms · 2026-06-27T04:27:07.605965+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

100 extracted references

  1. [1]

    N., Adelman-McCarthy, J

    Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543

    2009

  2. [2]

    D., Allende Prieto, C., et al

    Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12

    2015

  3. [3]

    G., Groves, B

    Allen, M. G., Groves, B. A., Dopita, M. A., Sutherland, R. S., & Kewley, L. J. 2008, ApJS, 178, 20

    2008

  4. [4]

    1993, ARA&A, 31, 473

    Antonucci, R. 1993, ARA&A, 31, 473

    1993

  5. [5]

    Arnaud, K. A. 1996, in Astronomical Society of the Pacific Conference Series, V ol. 101, Astronomical Data Analysis Software and Systems V , ed. G. H. Jacoby & J. Barnes, 17

    1996

  6. [6]

    1999, MNRAS, 310, 540

    Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540

    1999

  7. [7]

    F., & Gandhi, P

    Asmus, D., Hönig, S. F., & Gandhi, P. 2016, ApJ, 822, 109

    2016

  8. [8]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

    2009

  9. [9]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5

    1981

  10. [10]

    2024, A&A, 689, A125

    Bisigello, L., Gruppioni, C., Bolatto, A., et al. 2024, A&A, 689, A125

    2024

  11. [11]

    2000, A&A, 363, 476

    Bolzonella, M., Miralles, J.-M., & Pelló, R. 2000, A&A, 363, 476

    2000

  12. [12]

    2019, A&A, 622, A103

    Boquien, M., Burgarella, D., Roehlly, Y ., et al. 2019, A&A, 622, A103

    2019

  13. [13]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503

    2008

  14. [14]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151

    2004

  15. [15]

    2021, A&A, 654, A93

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

    2021

  16. [16]

    2005, MNRAS, 360, 1413

    Burgarella, D., Buat, V ., & Iglesias-Páramo, J. 2005, MNRAS, 360, 1413

    2005

  17. [17]

    2025, A&A, 699, A336

    Burgarella, D., Buat, V ., Theulé, P., et al. 2025, A&A, 699, A336

    2025

  18. [18]

    2008, A&A, 477, 735 Calabrò, A., Pentericci, L., Feltre, A., et al

    Caccianiga, A., Severgnini, P., Della Ceca, R., et al. 2008, A&A, 477, 735 Calabrò, A., Pentericci, L., Feltre, A., et al. 2023, A&A, 679, A80 Calistro Rivera, G., Lusso, E., Hennawi, J. F., & Hogg, D. W. 2016, ApJ, 833, 98

    2008

  19. [19]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682

    2000

  20. [20]

    & Emsellem, E

    Cappellari, M. & Emsellem, E. 2004, PASP, 116, 138

    2004

  21. [21]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Davé, R. 2018, MNRAS, 480, 4379

    2018

  22. [22]

    2023, Rev

    Chatzikos, M., Bianchi, S., Camilloni, F., et al. 2023, Rev. Mexicana Astron. Astrofis., 59, 327

    2023

  23. [23]

    & Charlot, S

    Chevallard, J. & Charlot, S. 2016, MNRAS, 462, 1415

    2016

  24. [24]

    2020, The Messenger, 180, 10

    Cirasuolo, M., Fairley, A., Rees, P., et al. 2020, The Messenger, 180, 10

    2020

  25. [25]

    2024, A&A, 684, A75 da Cunha, E., Charlot, S., & Elbaz, D

    Curti, M., Maiolino, R., Curtis-Lake, E., et al. 2024, A&A, 684, A75 da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595

    2024

  26. [26]

    2008, A&A, 485, 417

    Dadina, M. 2008, A&A, 485, 417

    2008

  27. [27]

    2020, MNRAS, 498, 4150 Article number, page 18 H

    Davies, R., Baron, D., Shimizu, T., et al. 2020, MNRAS, 498, 4150 Article number, page 18 H. Zhang (张浩) et al.: modeling AGN emission lines

    2020

  28. [28]

    Dopita, M. A. & Sutherland, R. S. 1995, ApJ, 455, 468

    1995

  29. [29]

    Dors, O. L. 2021, MNRAS, 507, 466

    2021

  30. [30]

    L., Maiolino, R., Cardaci, M

    Dors, O. L., Maiolino, R., Cardaci, M. V ., et al. 2020, MNRAS, 496, 3209

    2020

  31. [31]

    2016, MNRAS, 456, 3354

    Feltre, A., Charlot, S., & Gutkin, J. 2016, MNRAS, 456, 3354

    2016

  32. [32]

    Ferland, G. J. 2006, Hazy, A Brief Introduction to Cloudy 06.02

    2006

  33. [33]

    2006, MNRAS, 366, 767

    Fritz, J., Franceschini, A., & Hatziminaoglou, E. 2006, MNRAS, 366, 767

    2006

  34. [34]

    M., Goosmann, R

    Gaskell, C. M., Goosmann, R. W., Antonucci, R. R. J., & Whysong, D. H. 2004, ApJ, 616, 147

    2004

  35. [35]

    J., & Scott, P

    Grevesse, N., Asplund, M., Sauval, A. J., & Scott, P. 2010, Ap&SS, 328, 179

    2010

  36. [36]

    A., Dopita, M

    Groves, B. A., Dopita, M. A., & Sutherland, R. S. 2004, ApJS, 153, 9

    2004

  37. [37]

    2023, ApJ, 959, 39

    Harikane, Y ., Zhang, Y ., Nakajima, K., et al. 2023, ApJ, 959, 39

    2023

  38. [38]

    2023, MNRAS, 526, 3610 Hönig, S

    Hirschmann, M., Charlot, S., Feltre, A., et al. 2023, MNRAS, 526, 3610 Hönig, S. F. 2019, ApJ, 884, 171

    2023

  39. [39]

    F., Hernquist, L., Cox, T

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

    2006

  40. [40]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841

    2006

  41. [41]

    2024, A&A, 684, A153

    Iwasawa, K., Liu, T., Boller, T., et al. 2024, A&A, 684, A153

    2024

  42. [42]

    D., Leja, J., Conroy, C., & Speagle, J

    Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22 Juodžbalis, I., Conselice, C. J., Singh, M., et al. 2023, MNRAS, 525, 1353

    2021

  43. [43]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121

    2001

  44. [44]

    J., Nicholls, D

    Kewley, L. J., Nicholls, D. C., & Sutherland, R. S. 2019, ARA&A, 57, 511

    2019

  45. [45]

    D., Onoue, M., Inayoshi, K., et al

    Kocevski, D. D., Onoue, M., Inayoshi, K., et al. 2023, ApJ, 954, L4

    2023

  46. [46]

    G., Labbé, I., et al

    Kriek, M., van Dokkum, P. G., Labbé, I., et al. 2018, FAST: Fitting and As- sessment of Synthetic Templates, Astrophysics Source Code Library, record ascl:1803.008

    2018

  47. [47]

    S., et al

    Lambrides, E., Chiaberge, M., Long, A. S., et al. 2024, ApJ, 961, L25

    2024

  48. [48]

    J., & McDowell, J

    Laor, A., Fiore, F., Elvis, M., Wilkes, B. J., & McDowell, J. C. 1997, ApJ, 477, 93

    1997

  49. [49]

    D., Conroy, C., van Dokkum, P

    Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170

    2017

  50. [50]

    L., Greene, J

    Liu, G., Zakamska, N. L., Greene, J. E., Nesvadba, N. P. H., & Liu, X. 2013, MNRAS, 436, 2576

    2013

  51. [51]

    2017, ApJS, 232, 8

    Liu, T., Tozzi, P., Wang, J.-X., et al. 2017, ApJS, 232, 8

    2017

  52. [52]

    2010, Astrophysics and Space Science Proceedings, 16, 379 López, I

    Lodders, K. 2010, Astrophysics and Space Science Proceedings, 16, 379 López, I. E., Yang, G., Mountrichas, G., et al. 2024, A&A, 692, A209 López-Gonzaga, N., Burtscher, L., Tristram, K. R. W., Meisenheimer, K., &

    2010

  53. [53]

    2016, A&A, 591, A47

    Schartmann, M. 2016, A&A, 591, A47

    2016

  54. [54]

    & Rieke, G

    Lyu, J. & Rieke, G. H. 2018, ApJ, 866, 92

    2018

  55. [55]

    1998, AJ, 115, 2285

    Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285

    1998

  56. [56]

    2024, A&A, 691, A145

    Maiolino, R., Scholtz, J., Curtis-Lake, E., et al. 2024, A&A, 691, A145

    2024

  57. [57]

    & Hunt, L

    Marconi, A. & Hunt, L. K. 2003, ApJ, 589, L21 Martínez-Ramírez, L. N., Calistro Rivera, G., Lusso, E., et al. 2024, A&A, 688, A46

    2003

  58. [58]

    P., Brammer, G., et al

    Matthee, J., Naidu, R. P., Brammer, G., et al. 2024, ApJ, 963, 129

    2024

  59. [59]

    2024, A&A, 691, A345 Meléndez, M., Kraemer, S

    Mazzolari, G., Übler, H., Maiolino, R., et al. 2024, A&A, 691, A345 Meléndez, M., Kraemer, S. B., Armentrout, B. K., et al. 2008, ApJ, 682, 94

    2024

  60. [60]

    2013, pyCloudy: Tools to manage astronomical Cloudy photoion- ization code

    Morisset, C. 2013, pyCloudy: Tools to manage astronomical Cloudy photoion- ization code

    2013

  61. [61]

    2021, A&A, 646, A29

    Mountrichas, G., Buat, V ., Yang, G., et al. 2021, A&A, 646, A29

    2021

  62. [62]

    2025, A&A, 693, A50

    Napolitano, L., Castellano, M., Pentericci, L., et al. 2025, A&A, 693, A50

    2025

  63. [63]

    M., Nikutta, R., Ivezi ´c, Ž., & Elitzur, M

    Nenkova, M., Sirocky, M. M., Nikutta, R., Ivezi ´c, Ž., & Elitzur, M. 2008, ApJ, 685, 160

    2008

  64. [64]

    1987, MNRAS, 225, 55

    Netzer, H. 1987, MNRAS, 225, 55

    1987

  65. [65]

    2015, ARA&A, 53, 365

    Netzer, H. 2015, ARA&A, 53, 365

    2015

  66. [66]

    Nieva, M. F. & Przybilla, N. 2012, A&A, 539, A143

    2012

  67. [67]

    2009, A&A, 507, 1793

    Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793

    2009

  68. [68]

    Oh, K., Sarzi, M., Schawinski, K., & Yi, S. K. 2011, ApJS, 195, 13

    2011

  69. [69]

    M., Assef, R

    Padovani, P., Alexander, D. M., Assef, R. J., et al. 2017, A&A Rev., 25, 2

    2017

  70. [70]

    Pei, Y . C. 1992, ApJ, 395, 130

    1992

  71. [71]

    N., O’Brien, P., & Brinkmann, W

    Porquet, D., Reeves, J. N., O’Brien, P., & Brinkmann, W. 2004, A&A, 422, 85 Ramos Almeida, C. & Ricci, C. 2017, Nature Astronomy, 1, 679

    2004

  72. [72]

    J., et al

    Ricci, C., Trakhtenbrot, B., Koss, M. J., et al. 2017, ApJS, 233, 17

    2017

  73. [73]

    A., Kewley, L

    Rich, J. A., Kewley, L. J., & Dopita, M. A. 2014, ApJ, 781, L12

    2014

  74. [74]

    2023, A&A, 671, A33

    Sacchi, A., Risaliti, G., & Miniutti, G. 2023, A&A, 671, A33

    2023

  75. [75]

    L., et al

    Sarzi, M., Falcón-Barroso, J., Davies, R. L., et al. 2006, MNRAS, 366, 1151

    2006

  76. [76]

    2005, A&A, 437, 861

    Schartmann, M., Meisenheimer, K., Camenzind, M., Wolf, S., & Henning, T. 2005, A&A, 437, 861

    2005

  77. [77]

    2025, A&A, 697, A175

    Scholtz, J., Maiolino, R., D’Eugenio, F., et al. 2025, A&A, 697, A175

    2025

  78. [78]

    2017, Publications of the Astronomical Society of Australia, 34

    Spinoglio, L., Alonso-Herrero, A., Armus, L., et al. 2017, Publications of the Astronomical Society of Australia, 34

    2017

  79. [79]

    & Malkan, M

    Spinoglio, L. & Malkan, M. A. 1992, ApJ, 399, 504

    1992

  80. [80]

    2005, MNRAS, 361, 776

    Springel, V ., Di Matteo, T., & Hernquist, L. 2005, MNRAS, 361, 776

    2005

Showing first 80 references.