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arxiv: 2607.01832 · v1 · pith:C6VOXJXVnew · submitted 2026-07-02 · 🌌 astro-ph.GA

Post-starburst Galaxies with Active Galactic Nucleus: Properties and Evolutionary Sequences

Pith reviewed 2026-07-03 10:03 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords post-starburst galaxiesactive galactic nucleigalaxy quenchingMaNGA surveyradial profilesstellar kinematicsmerger remnantsevolutionary sequences
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The pith

Ring-like post-starburst galaxies can evolve into AGN-PSBs while high-mass central ones follow a merger-driven path, showing AGN feedback is not required for PSB formation.

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

This paper uses MaNGA integral-field data to classify post-starburst galaxies into AGN-hosting, central, ring-like, and irregular subtypes. It compares their stellar population gradients and kinematic properties against control samples. The radial profiles of mass-weighted age and V_star over sigma_star are interpreted as evidence that ring-like PSBs can develop central AGN activity later. High-mass central PSBs instead show higher merger rates and likely arise through a separate channel. The presence of PSB galaxies without AGN activity demonstrates that AGN feedback is not essential to produce the post-starburst spectral signature.

Core claim

Based on radial profiles of mass-weighted age and V_star/σ_star, RPSBs can evolve into AGN-PSBs, whereas H-CPSBs likely follow a distinct evolutionary pathway. The existence of RPSBs and IPSBs also indicates that AGN feedback is not a necessary condition for the formation of PSB.

What carries the argument

Radial profiles of mass-weighted stellar age and the stellar velocity-to-dispersion ratio V_star/σ_star, used to distinguish evolutionary sequences among post-starburst subtypes.

If this is right

  • RPSBs transition into AGN-PSBs as central activity develops.
  • H-CPSBs arise primarily through mergers and do not follow the same sequence.
  • AGN feedback is not required to form post-starburst galaxies, since RPSBs and IPSBs exist without it.
  • AGN-PSBs and RPSBs favor less violent external processes than the merger-dominated H-CPSBs.
  • All three subtypes display younger central stellar populations than their outskirts, opposite to control galaxies.

Where Pith is reading between the lines

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

  • If the proposed sequence is correct, AGN activity appears after the initial ring-like quenching rather than causing it.
  • The subtype distinctions suggest that spatially resolved data are needed to separate quenching channels that global spectra would mix.
  • Environmental effects such as ram-pressure stripping may dominate the ring-like channel, offering a testable contrast to merger-driven paths.

Load-bearing premise

That differences in radial profiles of mass-weighted age and V_star/σ_star between RPSBs and AGN-PSBs trace a temporal sequence rather than separate formation channels or selection biases in AGN identification.

What would settle it

Finding that RPSBs and AGN-PSBs exhibit identical mass-weighted age gradients with no kinematic or merger-fraction differences that would support a later-stage AGN phase.

Figures

Figures reproduced from arXiv: 2607.01832 by Cheng Li, Ho-Hin Leung, Junjie Huang, Qihang Cheng, Qiusheng Gu, Vivienne Wild, Yanmei Chen, Ying Yu, Yong Shi, Zhuo Cheng.

Figure 1
Figure 1. Figure 1: The relationship between the Hδ absorption line and the Hα emission line equivalent width for a sample of galaxies from the MaNGA DAP catalog, overlaid with the toy model evolutionary tracks. The solid tracks correspond exponentially declining SFHs with e-folding timescales ranging from τ = 0.5 Gyr (red) to τ = 5 Gyr (blue). The dashed tracks correspond to models featuring an extra starburst at 6.5 Gyr, fo… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of four PSBs in the MaNGA survey. The left panel shows the SDSS g, r, i-image. The middle panel colors PSB spaxels in red, non-PSB spaxels with median spectral S/N per pixel > 10 in blue, and spaxels with lower-S/N in grey; overlaid yellow lines denote the PSB regions identified by Cheng et al. (2024). The right-hand panel shows the spectrum at the location indicated by the black dot in the middle… view at source ↗
Figure 3
Figure 3. Figure 3: An example of AGN-PSB galaxy. Panel (a) displays the SDSS g, r, i-image. Panel (b) displays the spatially resolved [S ii]-BPT diagram, with Seyfert spaxels in pink, LINER spaxels in yellow, SF spaxels in blue. Panel (c) highlights the spatial distribution of spaxels: AGN-PSB spaxels in purple, traditional PSB spaxels in red, spaxels of median spectral S/N > 10 per pixel in blue, and lower S/N spaxels in gr… view at source ↗
Figure 4
Figure 4. Figure 4: The median radial profiles of log(L[O III], 1kpc) for AGN-PSBs (purple solid line) and their controls (black dashed line). The error bars show the 30th to 70th percentile of the distribution for the AGN-PSBs. (c) displays the distributions of global Dn4000. RPSBs have the youngest stellar populations with a median Dn4000 of 1.38, while CPSBs and AGN-PSBs have me￾dian global Dn4000 of 1.49 and 1.45, respect… view at source ↗
Figure 5
Figure 5. Figure 5: The distribution of three PSB sub-types on the global Dn4000–stellar mass relation. The background contours represent the full MaNGA sample, and the two dashed lines divide galaxies into star-forming (SF), green-valley (GV), and quiescent-sequence (QS) galaxies. The lower dashed line approximates the upper boundary of the SF main sequence (at the ∼1σ scatter level), while the upper dashed line marks the lo… view at source ↗
Figure 6
Figure 6. Figure 6: Fraction of CPSBs (red circles), RPSBs (blue squares), AGN-PSBs (purple triangles) and the total PSB population (AGN-PSBs+CPSBs+RPSBs) (black diamonds) as a function of stellar mass and g − r. The error on the x-axis is defined by the parameter binsize, while the error on the y-axis is estimated through bootstrap resampling. less consistent with a major merger driven origin, as major mergers would be expec… view at source ↗
Figure 7
Figure 7. Figure 7: An example of low-mass CPSB galaxy with log(M∗/M⊙) = 9.37. Panel (a) displays the SDSS g, r, i-image. Panel (b) shows the Dn4000 map, where bluer color indicate smaller values and redder color indicate larger values, ranging from 1.0 to 1.8. Panel (c) displays PSB spaxels in red, spaxels of median spectral S/N > 10 per pixel in blue, lower S/N spaxels in gray, and outlines the PSB region defined by Cheng e… view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of global properties for H-CPSBs (red), RPSBs (blue), and AGN-PSBs (purple). The top row shows histograms of Re (panel a), S´ersic index (panel b), and g − r (panel c). Vertical lines at the top of each panel mark the median of each distribution. The bottom row compares each PSB sub-type with its control sample in Re (panel d), S´ersic index (panel e), and g − r color (panel f). Colored solid… view at source ↗
Figure 9
Figure 9. Figure 9: The median radial profiles of Dn4000 (top row), HδA (middle row), and W(Hα) (bottom row) as a function of radius for AGN-PSB (left column; purple solid line), H-CPSB (middle column; red solid line), RPSB (right column; blue solid line) and their control samples (black dashed line). The error bars show the 30th to 70th percentile of the distribution for PSB sub-types. For direct comparison, the median gradi… view at source ↗
Figure 10
Figure 10. Figure 10: The median radial profiles of light-weighted age (top row) and mass-weighted age (bottom row) as a function of radius for AGN-PSB (left column; purple solid line), H-CPSB (middle column; red solid line), RPSB (right column; blue solid line) and their control samples (black dashed line). The error bars show the 30th to 70th percentile of the distribution for PSB sub-types. For direct comparison, the median… view at source ↗
Figure 11
Figure 11. Figure 11: The median radial profiles of Vstar/σstar as a function of radius for AGN-PSB (left column; purple solid line), H-CPSB (middle column; red solid line), RPSB (right column; blue solid line) and their control samples (black dashed line). The error bars show the 30th to 70th percentile of the distribution for PSB sub-types. For direct comparison, the median gradient of AGN-PSBs is overlaid in the middle and … view at source ↗
Figure 12
Figure 12. Figure 12: Median radial profiles of Dn4000 (a), HδA (b), log W(Hα) (c), light-weighted age (d), mass-weighted age (e), and Vstar/σstar (f) as a function of radius for H-CPSBs (red), AGN-PSBs selected with HδA > 3 (pink), 4 (magenta), and 5 (purple), respectively. However, we cannot exclude the possibility that AGN feedback may still play a role at later stages, potentially contributing to the transition from the PS… view at source ↗
read the original abstract

Post-starburst (PSB) galaxies, identified by strong Balmer absorption and weak nebular emission, provide a key laboratory for studying rapid quenching. Using the final data release of the SDSS-IV MaNGA survey, we follow the traditional PSB selection criteria of Chen et al. (2019) and develop a new method to identify regions that simultaneously exhibit PSB features and nuclear activities (AGN-PSBs). Our final sample comprises 48 AGN-PSBs, 92 central PSBs (CPSBs), 89 ring-like PSBs (RPSBs), and 828 irregular PSBs (IPSBs). We find the global and spatially resolved properties of CPSBs and RPSBs are consistent with the results of Chen et al. (2019). In this work, we focus on the properties of AGN-PSBs, comparing them with CPSBs, RPSBs, and control galaxies. Similar to CPSBs and RPSBs, AGN-PSBs show positive $\mathrm{D}_{n}4000$ gradients relative to negative $\mathrm{D}_{n}4000$ gradients of their controls, which indicates younger stellar populations in the central region than that in the outskirt. Among the three sub-types, high-mass CPSBs (H-CPSBs, with $\log(M_{*}/M_{\odot})>9.5$) display the highest incidence of merger remnants and gas--star kinematic misalignment, consistent with a merger/interaction-dominated origin. AGN-PSBs and RPSBs, however, show lower and comparable fractions of merger remnants and gas--star kinematic misalignment, favoring less violent external mechanisms. Based on radial profiles of mass-weighted age and $V_{\rm star}/\sigma_{\rm star}$, we suggest that RPSBs can evolve into AGN-PSBs, whereas H-CPSBs likely follow a distinct evolutionary pathway. The existence of RPSBs and IPSBs also indicates that AGN feedback is not a necessary condition for the formation of PSB.

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 uses the final MaNGA release to select 48 AGN-PSBs (via a new method combining PSB spectral features with nuclear activity), 92 CPSBs, 89 RPSBs, and 828 IPSBs following Chen et al. (2019) criteria. It compares global and spatially resolved properties (Dn4000 gradients, merger fractions, gas-star misalignment) across subtypes and controls, finding positive Dn4000 gradients in all PSB types. Based on radial profiles of mass-weighted age and V_star/σ_star, it proposes that RPSBs evolve into AGN-PSBs while high-mass CPSBs follow a distinct merger-driven path, and concludes that AGN feedback is not required for PSB formation.

Significance. If the proposed evolutionary links are robust, the work would clarify the diversity of quenching pathways in PSBs and the non-essential role of AGN feedback, extending Chen et al. (2019) with spatially resolved data and an AGN-PSB subsample. The analysis employs standard methods on public MaNGA data and reports consistent global properties with prior samples.

major comments (2)
  1. [Abstract] Abstract: the inference that differences in radial profiles of mass-weighted age and V_star/σ_star indicate RPSBs evolve into AGN-PSBs (rather than reflecting distinct formation channels or AGN-selection biases) is load-bearing for the central claim but rests on single-epoch observations without quantitative tests (e.g., matched controls or forward modeling) to rule out alternatives.
  2. [Results on radial profiles and evolutionary inference] The section discussing radial profiles and evolutionary sequences: the claim that AGN-PSBs and RPSBs favor less violent mechanisms (lower merger fractions) is used to support the sequence, yet no explicit check is shown for whether AGN-PSB identification preferentially selects centrally concentrated young populations that could produce the observed profile differences by construction.
minor comments (2)
  1. [Abstract] Abstract: no error bars, uncertainties, or robustness metrics are provided for the key profile comparisons or incidence rates.
  2. Notation for V_star/σ_star and mass-weighted age should be defined at first use with reference to the exact MaNGA-derived quantities.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below with clarifications on our analysis and proposed revisions to better contextualize the evolutionary inferences and address potential biases.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the inference that differences in radial profiles of mass-weighted age and V_star/σ_star indicate RPSBs evolve into AGN-PSBs (rather than reflecting distinct formation channels or AGN-selection biases) is load-bearing for the central claim but rests on single-epoch observations without quantitative tests (e.g., matched controls or forward modeling) to rule out alternatives.

    Authors: We acknowledge that the proposed evolutionary link from RPSBs to AGN-PSBs is an interpretation drawn from single-epoch MaNGA observations. It is supported by the close similarity in mass-weighted age and V_star/σ_star radial profiles between these two subtypes (distinct from H-CPSBs), together with their comparably low merger fractions and gas-star misalignments. We cannot add forward modeling or new matched-control simulations within the scope of this work. We will revise the abstract and discussion to present the sequence as a suggested pathway based on observed similarities, while explicitly discussing alternative interpretations including distinct channels and AGN-selection effects, and softening the language accordingly. revision: partial

  2. Referee: [Results on radial profiles and evolutionary inference] The section discussing radial profiles and evolutionary sequences: the claim that AGN-PSBs and RPSBs favor less violent mechanisms (lower merger fractions) is used to support the sequence, yet no explicit check is shown for whether AGN-PSB identification preferentially selects centrally concentrated young populations that could produce the observed profile differences by construction.

    Authors: The AGN-PSB selection combines PSB spectral features with nuclear activity, which could in principle favor central concentrations. However, all PSB subtypes (including CPSBs and RPSBs) exhibit positive Dn4000 gradients, and the mass-weighted age profiles are compared across the full samples. We will add an explicit discussion and supplementary check in the results section examining whether profile differences persist after accounting for central concentration metrics, to demonstrate that the observed distinctions are not solely by construction of the AGN-PSB identification. revision: yes

standing simulated objections not resolved
  • We cannot perform forward modeling, hydrodynamic simulations, or extensive new quantitative matched-control tests to definitively rule out alternatives to the evolutionary sequence, as these lie beyond the observational scope and resources of the current study.

Circularity Check

0 steps flagged

No circularity: observational comparisons and interpretive suggestion remain independent of inputs

full rationale

The paper reports sample selection via established criteria from Chen et al. (2019), then compares global and resolved properties (Dn4000 gradients, merger fractions, kinematic misalignment, mass-weighted age and Vstar/σstar profiles) across AGN-PSBs, CPSBs, RPSBs and controls drawn from MaNGA. The evolutionary suggestion is presented as an inference from profile differences rather than any equation, fit, or self-citation that reduces the claim to its own inputs by construction. No fitted parameters are renamed as predictions, no uniqueness theorems are invoked, and the self-citation is limited to sample definition and is not load-bearing for the central interpretive step. The derivation chain is therefore self-contained against external data.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard domain assumptions about spectral diagnostics for stellar age and AGN activity plus the prior selection criteria; the mass threshold for H-CPSBs is a classification choice carried from literature.

free parameters (1)
  • high-mass threshold = log(M*/M_sun) > 9.5
    log(M*/M_sun) > 9.5 used to separate H-CPSBs and highlight merger differences; chosen to match prior work or emphasize trends.
axioms (1)
  • domain assumption Traditional PSB selection criteria of Chen et al. (2019) correctly isolate post-starburst spectral features without significant contamination.
    Paper states it follows these criteria directly for the base sample.

pith-pipeline@v0.9.1-grok · 5927 in / 1444 out tokens · 58418 ms · 2026-07-03T10:03:47.041462+00:00 · methodology

discussion (0)

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Works this paper leans on

106 extracted references · 105 canonical work pages · 10 internal anchors

  1. [1]

    L., Appleton, P

    Alatalo, K., Cales, S. L., Appleton, P. N., et al. 2014, ApJL, 794, L13, doi: 10.1088/2041-8205/794/1/L13

  2. [2]

    L., Rich, J

    Alatalo, K., Cales, S. L., Rich, J. A., et al. 2016, ApJS, 224, 38, doi: 10.3847/0067-0049/224/2/38

  3. [3]

    2023, VizieR Online Data Catalog: BPT class and AGN classification (Alban+, 2023), VizieR On-line Data Catalog: J/A+A/674/A85

    Alban, M., & Wylezalek, D. 2023, VizieR Online Data Catalog: BPT class and AGN classification (Alban+, 2023), VizieR On-line Data Catalog: J/A+A/674/A85. Originally published in: 2023A&A...674A..85A

  4. [4]

    2025, MNRAS, 539, 3568, doi: 10.1093/mnras/staf659 22

    Almaini, O., Wild, V., Maltby, D., et al. 2025, MNRAS, 539, 3568, doi: 10.1093/mnras/staf659 22

  5. [5]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681, doi: 10.1086/380092

  6. [6]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5, doi: 10.1086/130766

  7. [7]

    J., Springel, V., White, S

    Goto, T. 2005, MNRAS, 360, 587, doi: 10.1111/j.1365-2966.2005.09047.x

  8. [8]

    X., & F¨ orster Schreiber, N

    Baron, D., Netzer, H., Poznanski, D., Prochaska, J. X., & F¨ orster Schreiber, N. M. 2017, MNRAS, 470, 1687, doi: 10.1093/mnras/stx1329

  9. [9]

    X., et al

    Baron, D., Netzer, H., Prochaska, J. X., et al. 2018, MNRAS, 480, 3993, doi: 10.1093/mnras/sty2113

  10. [10]

    J., Springel, V., White, S

    Bekki, K., Couch, W. J., Shioya, Y., & Vazdekis, A. 2005, MNRAS, 359, 949, doi: 10.1111/j.1365-2966.2005.08932.x

  11. [11]

    F., Wolf, C., Meisenheimer, K., et al

    Bell, E. F., Wolf, C., Meisenheimer, K., et al. 2004, ApJ, 608, 752, doi: 10.1086/420778

  12. [12]

    , keywords =

    Blake, C., Pracy, M. B., Couch, W. J., et al. 2004, MNRAS, 355, 713, doi: 10.1111/j.1365-2966.2004.08351.x

  13. [13]

    R., Bershady, M

    Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, AJ, 154, 28, doi: 10.3847/1538-3881/aa7567

  14. [14]

    S., et al

    Brough, S., van de Sande, J., Owers, M. S., et al. 2017, ApJ, 844, 59, doi: 10.3847/1538-4357/aa7a11

  15. [15]

    Brown, M. J. I., Dey, A., Jannuzi, B. T., et al. 2007, ApJ, 654, 858, doi: 10.1086/509652 Bruzual A., G. 1983, ApJ, 273, 105, doi: 10.1086/161352

  16. [16]

    A., Law, D

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7, doi: 10.1088/0004-637X/798/1/7

  17. [17]

    2001, PASP, 113, 1449, doi: 10.1086/324269

    Calzetti, D. 2001, PASP, 113, 1449, doi: 10.1086/324269

  18. [18]

    2004, PASP, 116, 138, doi: 10.1086/381875

    Cappellari, M., & Emsellem, E. 2004, PASP, 116, 138, doi: 10.1086/381875

  19. [19]

    2022, ApJ, 933, 228, doi: 10.3847/1538-4357/ac75b4

    Chen, X., Lin, Z., Kong, X., et al. 2022, ApJ, 933, 228, doi: 10.3847/1538-4357/ac75b4

  20. [20]

    A., et al

    Chen, Y.-M., Shi, Y., Tremonti, C. A., et al. 2016, Nature Communications, 7, 13269, doi: 10.1038/ncomms13269

  21. [21]

    2019, MNRAS, 489, 5709, doi: 10.1093/mnras/stz2494

    Chen, Y.-M., Shi, Y., Wild, V., et al. 2019, MNRAS, 489, 5709, doi: 10.1093/mnras/stz2494

  22. [22]

    2024, ApJ, 961, 216, doi: 10.3847/1538-4357/ad1510

    Cheng, Z., Li, C., Li, N., Yan, R., & Mo, H. 2024, ApJ, 961, 216, doi: 10.3847/1538-4357/ad1510

  23. [23]

    2019, MNRAS, 484, 5192, doi: 10.1093/mnras/stz349

    Chown, R., Li, C., Athanassoula, E., et al. 2019, MNRAS, 484, 5192, doi: 10.1093/mnras/stz349

  24. [24]

    J., & Sharples, R

    Couch, W. J., & Sharples, R. M. 1987, MNRAS, 229, 423, doi: 10.1093/mnras/229.3.423

  25. [25]

    Schechter, P. L. 1983, ApJ, 266, 41, doi: 10.1086/160757

  26. [26]

    A., van de Voort, F., Rowlands, K., et al

    Davis, T. A., van de Voort, F., Rowlands, K., et al. 2019, MNRAS, 484, 2447, doi: 10.1093/mnras/stz180 D’Eugenio, F., P´ erez-Gonz´ alez, P. G., Maiolino, R., et al. 2024, Nature Astronomy, 8, 1443, doi: 10.1038/s41550-024-02345-1

  27. [27]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

  28. [28]

    Dressler, A., & Gunn, J. E. 1983, ApJ, 270, 7, doi: 10.1086/161093

  29. [29]

    M., et al

    Dressler, A., Smail, I., Poggianti, B. M., et al. 1999, ApJS, 122, 51, doi: 10.1086/313213

  30. [30]

    A., et al

    Drory, N., MacDonald, N., Bershady, M. A., et al. 2015, AJ, 149, 77, doi: 10.1088/0004-6256/149/2/77

  31. [31]

    2024, The Open Journal of Astrophysics, 7, 121, doi: 10.33232/001c.127779

    Ellison, S., Ferreira, L., Wild, V., et al. 2024, The Open Journal of Astrophysics, 7, 121, doi: 10.33232/001c.127779

  32. [32]

    L., Wilkinson, S., Woo, J., et al

    Ellison, S. L., Wilkinson, S., Woo, J., et al. 2022, MNRAS, 517, L92, doi: 10.1093/mnrasl/slac109

  33. [33]

    , keywords =

    Emsellem, E., Cappellari, M., Krajnovi´ c, D., et al. 2007, MNRAS, 379, 401, doi: 10.1111/j.1365-2966.2007.11752.x —. 2011, MNRAS, 414, 888, doi: 10.1111/j.1365-2966.2011.18496.x

  34. [34]

    2019, MNRAS, 483, 2057, doi: 10.1093/mnras/sty3135

    Fischer, J.-L., Dom´ ınguez S´ anchez, H., & Bernardi, M. 2019, MNRAS, 483, 2057, doi: 10.1093/mnras/sty3135

  35. [35]

    French, K. D. 2021, PASP, 133, 072001, doi: 10.1088/1538-3873/ac0a59

  36. [36]

    2017, MNRAS, 466, 1903, doi: 10.1093/mnras/stw3209

    Gatto, A., Walch, S., Naab, T., et al. 2017, MNRAS, 466, 1903, doi: 10.1093/mnras/stw3209

  37. [37]

    J., Springel, V., White, S

    Goto, T. 2005, MNRAS, 357, 937, doi: 10.1111/j.1365-2966.2005.08701.x —. 2006, MNRAS, 369, 1765, doi: 10.1111/j.1365-2966.2006.10413.x —. 2007a, MNRAS, 381, 187, doi: 10.1111/j.1365-2966.2007.12227.x —. 2007b, MNRAS, 377, 1222, doi: 10.1111/j.1365-2966.2007.11674.x

  38. [38]

    M., Tremonti, C., et al

    Goto, T., Yamauchi, C., Fujita, Y., et al. 2003, MNRAS, 346, 601, doi: 10.1046/j.1365-2966.2003.07114.x

  39. [39]

    W., Croom, S

    Green, A. W., Croom, S. M., Scott, N., et al. 2018, MNRAS, 475, 716, doi: 10.1093/mnras/stx3135

  40. [40]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332, doi: 10.1086/500975

  41. [41]

    Puxley, P. J. 1986, MNRAS, 221, 41P, doi: 10.1093/mnras/221.1.41P

  42. [42]

    W., Masjedi, M., Berlind, A

    Hogg, D. W., Masjedi, M., Berlind, A. A., et al. 2006, ApJ, 650, 763, doi: 10.1086/507172

  43. [43]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2006, ApJS, 163, 1, doi: 10.1086/499298

  44. [44]

    F., Kereˇ s, D., O˜ norbe, J., et al

    Hopkins, P. F., Kereˇ s, D., O˜ norbe, J., et al. 2014, MNRAS, 445, 581, doi: 10.1093/mnras/stu1738

  45. [45]

    J., Le F` evre, O., et al

    Ilbert, O., McCracken, H. J., Le F` evre, O., et al. 2013, A&A, 556, A55, doi: 10.1051/0004-6361/201321100

  46. [46]

    1977, ApJL, 218, L43, doi: 10.1086/182572 23

    Illingworth, G. 1977, ApJL, 218, L43, doi: 10.1086/182572 23

  47. [47]

    2014, ApJ, 787, 63, doi: 10.1088/0004-637X/787/1/63

    Jin, S.-W., Gu, Q., Huang, S., Shi, Y., & Feng, L.-L. 2014, ApJ, 787, 63, doi: 10.1088/0004-637X/787/1/63

  48. [48]

    M., Tremonti, C., et al

    Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055, doi: 10.1111/j.1365-2966.2003.07154.x Kereˇ s, D., Katz, N., Weinberg, D. H., & Dav´ e, R. 2005, MNRAS, 363, 2, doi: 10.1111/j.1365-2966.2005.09451.x

  49. [49]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121, doi: 10.1086/321545

  50. [50]

    G., Benson, A

    Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961, doi: 10.1111/j.1365-2966.2006.10859.x

  51. [51]

    D., Lemaux, B

    Kocevski, D. D., Lemaux, B. C., Lubin, L. M., et al. 2011, ApJL, 737, L38, doi: 10.1088/2041-8205/737/2/L38 Krajnovi´ c, D., Cappellari, M., de Zeeuw, P. T., & Copin, Y. 2006, MNRAS, 366, 787, doi: 10.1111/j.1365-2966.2005.09902.x

  52. [52]

    Lagos, C. d. P. 2018, arXiv e-prints, arXiv:1810.13074, doi: 10.48550/arXiv.1810.13074

  53. [53]

    C., et al

    Lanz, L., Stepanoff, S., Hickox, R. C., et al. 2022, ApJ, 935, 29, doi: 10.3847/1538-4357/ac7d56

  54. [54]

    R., Yan, R., Bershady, M

    Law, D. R., Yan, R., Bershady, M. A., et al. 2015, AJ, 150, 19, doi: 10.1088/0004-6256/150/1/19

  55. [55]

    R., Cherinka, B., Yan, R., et al

    Law, D. R., Cherinka, B., Yan, R., et al. 2016, AJ, 152, 83, doi: 10.3847/0004-6256/152/4/83

  56. [56]

    2024, MNRAS, 528, 4029, doi: 10.1093/mnras/stae225

    Leung, H.-H., Wild, V., Papathomas, M., et al. 2024, MNRAS, 528, 4029, doi: 10.1093/mnras/stae225

  57. [57]

    2025, MNRAS, 543, 738, doi: 10.1093/mnras/staf1493

    Chen, Y. 2025, MNRAS, 543, 738, doi: 10.1093/mnras/staf1493

  58. [58]

    2021, MNRAS, 501, 14, doi: 10.1093/mnras/staa3618

    Li, S.-l., Shi, Y., Bizyaev, D., et al. 2021, MNRAS, 501, 14, doi: 10.1093/mnras/staa3618

  59. [59]

    2017, ApJ, 838, 105, doi: 10.3847/1538-4357/aa657a

    Lin, L., Li, C., He, Y., Xiao, T., & Wang, E. 2017, ApJ, 838, 105, doi: 10.3847/1538-4357/aa657a

  60. [60]

    T., Almaini, O., Wild, V., et al

    Maltby, D. T., Almaini, O., Wild, V., et al. 2018, MNRAS, 480, 381, doi: 10.1093/mnras/sty1794

  61. [61]

    T., Almaini, O., McLure, R

    Maltby, D. T., Almaini, O., McLure, R. J., et al. 2019, MNRAS, 489, 1139, doi: 10.1093/mnras/stz2211

  62. [62]

    2015, A&A, 582, A37, doi: 10.1051/0004-6361/201526187

    Melnick, J., Telles, E., De Propris, R., & Chu, Z.-H. 2015, A&A, 582, A37, doi: 10.1051/0004-6361/201526187

  63. [63]

    2013, ApJ, 777, 18, doi: 10.1088/0004-637X/777/1/18

    Muzzin, A., Marchesini, D., Stefanon, M., et al. 2013, ApJ, 777, 18, doi: 10.1088/0004-637X/777/1/18

  64. [64]

    2014, MNRAS, 444, 3357, doi: 10.1093/mnras/stt1919

    Naab, T., Oser, L., Emsellem, E., et al. 2014, MNRAS, 444, 3357, doi: 10.1093/mnras/stt1919

  65. [65]

    M., et al

    Paccagnella, A., Vulcani, B., Poggianti, B. M., et al. 2019, MNRAS, 482, 881, doi: 10.1093/mnras/sty2728

  66. [66]

    M., McAlpine, S., Trayford, J

    Pawlik, M. M., McAlpine, S., Trayford, J. W., et al. 2019, Nature Astronomy, 3, 440, doi: 10.1038/s41550-019-0725-z

  67. [67]

    M., Wild, V., Walcher, C

    Pawlik, M. M., Wild, V., Walcher, C. J., et al. 2016, MNRAS, 456, 3032, doi: 10.1093/mnras/stv2878

  68. [68]

    M., Taj Aldeen, L., Wild, V., et al

    Pawlik, M. M., Taj Aldeen, L., Wild, V., et al. 2018, MNRAS, 477, 1708, doi: 10.1093/mnras/sty589

  69. [69]

    M., Smail, I., Dressler, A., et al

    Poggianti, B. M., Smail, I., Dressler, A., et al. 1999, ApJ, 518, 576, doi: 10.1086/307322

  70. [70]

    D., Hogg, D

    Quintero, A. D., Hogg, D. W., Blanton, M. R., et al. 2004, ApJ, 602, 190, doi: 10.1086/380601

  71. [71]

    Pipe3D, a pipeline to analyze Integral Field Spectroscopy data: I. New fitting phylosophy of FIT3D

    Rowlands, K., Wild, V., Bourne, N., et al. 2018, MNRAS, 473, 1168, doi: 10.1093/mnras/stx1903 S´ anchez, S. F., P´ erez, E., S´ anchez-Bl´ azquez, P., et al. 2016a, RMxAA, 52, 21, doi: 10.48550/arXiv.1509.08552 —. 2016b, RMxAA, 52, 171, doi: 10.48550/arXiv.1602.01830 S´ anchez-Bl´ azquez, P., Peletier, R. F., Jim´ enez-Vicente, J., et al. 2006, MNRAS, 371...

  72. [72]

    2021, ApJ, 919, 134, doi: 10.3847/1538-4357/ac0f7f

    Sazonova, E., Alatalo, K., Rowlands, K., et al. 2021, ApJ, 919, 134, doi: 10.3847/1538-4357/ac0f7f

  73. [73]

    , keywords =

    Schmitt, H. R., Storchi-Bergmann, T., & Cid Fernandes, R. 1999, MNRAS, 303, 173, doi: 10.1046/j.1365-8711.1999.02203.x

  74. [74]

    J., Verrico, M., Bezanson, R., et al

    Setton, D. J., Verrico, M., Bezanson, R., et al. 2022, ApJ, 931, 51, doi: 10.3847/1538-4357/ac6096

  75. [75]

    A., Gunn, J

    Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32, doi: 10.1088/0004-6256/146/2/32

  76. [76]

    2011, ApJ, 741, 77, doi: 10.1088/0004-637X/741/2/77

    Jonsson, P. 2011, ApJ, 741, 77, doi: 10.1088/0004-637X/741/2/77

  77. [77]

    A., et al

    Socolovsky, M., Almaini, O., Hatch, N. A., et al. 2018, MNRAS, 476, 1242, doi: 10.1093/mnras/sty312

  78. [78]

    H., Franx, M., Illingworth, G., Kelson, D

    Tran, K.-V. H., Franx, M., Illingworth, G., Kelson, D. D., & van Dokkum, P. 2003, ApJ, 599, 865, doi: 10.1086/379804

  79. [79]

    H., Franx, M., Illingworth, G

    Tran, K.-V. H., Franx, M., Illingworth, G. D., et al. 2004, ApJ, 609, 683, doi: 10.1086/421237

  80. [80]

    A., Moustakas, J., & Diamond-Stanic, A

    Tremonti, C. A., Moustakas, J., & Diamond-Stanic, A. M. 2007, ApJL, 663, L77, doi: 10.1086/520083 van de Sande, J., Bland-Hawthorn, J., Brough, S., et al. 2017, MNRAS, 472, 1272, doi: 10.1093/mnras/stx1751

Showing first 80 references.