pith. sign in

arxiv: 2509.12316 · v3 · submitted 2025-09-15 · 🌌 astro-ph.GA

Clues to inside-out quenching in quiescent galaxies at 1.2lesssim zlesssim2.2: Age, Fe-, and Mg-abundance gradients from JWST-SUSPENSE

Chloe M. Cheng , Martje Slob , Mariska Kriek , Aliza G. Beverage , Guillermo Barro , Rachel Bezanson , Anna de Graaff , Natascha M. F\"orster Schreiber
show 11 more authors
Brian Lorenz Danilo Marchesini Ignacio Mart\'in-Navarro Adam Muzzin Andrew B. Newman Sedona H. Price Katherine A. Suess Arjen van der Wel Jesse van de Sande Pieter G. van Dokkum Daniel R. Weisz
This is my paper

Pith reviewed 2026-05-18 15:51 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords quiescent galaxiesstellar population gradientsage gradientschemical abundance gradientsinside-out quenchinghigh-redshift galaxiesJWST spectroscopy
0
0 comments X p. Extension

The pith

Distant quiescent galaxies have older cores with flat iron abundances, suggesting inside-out quenching.

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

The paper derives age and abundance gradients for eight massive quiescent galaxies at redshifts from 1.2 to 2.2 using JWST NIRSpec spectroscopy. It reports negative age gradients, flat iron abundance gradients, and tentative positive magnesium abundance and magnesium-to-iron gradients. These findings indicate that the galaxy centers are older and possibly magnesium-deficient relative to the outer parts. Such patterns support the idea of inside-out quenching, where central regions form stars and then stop forming them earlier than the outskirts. The observations contrast with trends seen in nearby galaxies and point to differences in how these distant systems assembled.

Core claim

By fitting full-spectrum models to NIRSpec-MSA spectroscopy, the galaxies exhibit negative age gradients and flat [Fe/H] gradients, with tentative positive [Mg/H] and [Mg/Fe] gradients. This indicates that galaxy cores are older and perhaps Mg-deficient compared to their outskirts, consistent with inside-out quenching where cores form faster and quench more efficiently.

What carries the argument

Full-spectrum fitting of ultra-deep NIRSpec-MSA spectroscopy to extract radial gradients in stellar age, [Fe/H], [Mg/H], and [Mg/Fe].

If this is right

  • Galaxy cores formed faster and quenched earlier than the outskirts.
  • Age gradients show a positive trend with rotational support.
  • Marginal trends exist between [Fe/H] gradients and both velocity dispersions and galaxy ages.
  • These high-redshift patterns differ from those in lower-redshift quiescent galaxies, which show flat age gradients and negative metallicity gradients.

Where Pith is reading between the lines

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

  • Rapid gas expulsion could explain the magnesium-deficient cores as a central quenching mechanism.
  • Minor mergers may contribute to younger stellar populations in the outskirts.
  • Progenitor bias could affect the observed gradients as the galaxy population evolves over time.

Load-bearing premise

The full-spectrum fitting procedure accurately breaks the age-metallicity degeneracy and recovers unbiased radial gradients despite potential effects from dust or model assumptions.

What would settle it

Independent measurements using different spectral models or direct comparison to color gradients from photometry that fail to recover the same age and abundance patterns would challenge the reported gradients.

Figures

Figures reproduced from arXiv: 2509.12316 by Adam Muzzin, Aliza G. Beverage, Andrew B. Newman, Anna de Graaff, Arjen van der Wel, Brian Lorenz, Chloe M. Cheng, Daniel R. Weisz, Danilo Marchesini, Guillermo Barro, Ignacio Mart\'in-Navarro, Jesse van de Sande, Katherine A. Suess, Mariska Kriek, Martje Slob, Natascha M. F\"orster Schreiber, Pieter G. van Dokkum, Rachel Bezanson, Sedona H. Price.

Figure 1
Figure 1. Figure 1: Colour image cutouts of the 8 distant, quiescent galaxies in our sample. Seven galaxies have NIRCam imaging, which we combine into RGB images here. Galaxy 130040 does not have NIRCam imaging, so we show a cutout of the COSMOS HST/ACS F814W image (Koekemoer et al. 2007; Scoville et al. 2007, note that this image is rotated by 90 degrees compared to the NIRCam images). We highlight the regions that we combin… view at source ↗
Figure 2
Figure 2. Figure 2: Top: Stack of continuum-normalized spectra of all galaxies. The integrated stack is shown in green, the core stack is shown in blue, and the outskirt stack is shown in magenta. We label key spectral features. In the inset panels, we zoom-in on the Hβ (∼ 4849 − 4878 Å, sensitive to age), Mgb (∼ 5162 − 5194 Å, sensitive to [Mg/H]), Fe52 (∼ 5247 − 5287 Å, sensitive to age and [Fe/H]), and Fe53 (∼ 5314 − 5454 … view at source ↗
Figure 3
Figure 3. Figure 3: Best-fitting alfα models to our quiescent galaxy spectra. The core spectra (grey lines) are shown in the top panel in order of increasing redshift from top to bottom, with their best-fit models overplotted (blue lines). The outskirt spectra (grey lines) and fits (magenta lines) are similarly shown in the bottom panel. We normalise each spectrum by its median value between 4480 − 4520 Å and arbitrarily offs… view at source ↗
Figure 4
Figure 4. Figure 4: Spatially-resolved stellar population parameters derived from our alfα full spectrum fits. In the left column, we plot our measured pa￾rameters as a function of de-projected radius in units of Re (determined as described in Section 3.3). In the right column, we plot our measured parameters as a function of de-projected radius in units of kpc. We show age in the top row, [Fe/H] in the second row, [Mg/Fe] in… view at source ↗
Figure 5
Figure 5. Figure 5: Spatially resolved age and [Fe/H] gradients, normalized by Re (see Section 3.3), as a function of galaxy parameters. We show the age gradients in the top row and the [Fe/H] gradients in the bottom row. The outlying object with a strongly negative age gradient is 127154. We show the integrated velocity dispersion (σ, from our alfα fits to the integrated spectra) in the left column, the integrated age (from … view at source ↗
Figure 6
Figure 6. Figure 6: Individual core and outskirt elemental abundances. In the first three panels, we show formation time on the x-axis, which we compute by correcting the stellar age (from our alfα fits to the integrated spectra) by the age of the Universe at the redshift of each galaxy. We show our [Fe/H] abundances in the first panel, [Mg/H] abundances in the second panel, and [Mg/Fe] abundances in the third panel. In the f… view at source ↗
read the original abstract

[Abridged] Spatially resolved stellar populations of massive quiescent galaxies at cosmic noon provide powerful insights into quenching and assembly mechanisms. Previous photometric studies have revealed that the cores of these galaxies are redder than their outskirts. However, spectroscopy is needed to break the age-metallicity degeneracy and uncover the driver of colour gradients. We derive age and elemental abundance gradients for eight distant ($1.2 \lesssim z \lesssim 2.2$), massive ($10.3\lesssim\log({\rm M}_*/{\rm M}_\odot)\lesssim 11.1$) quiescent galaxies by fitting full-spectrum models to ultra-deep NIRSpec-MSA spectroscopy from the JWST-SUSPENSE survey. We find that these galaxies have negative age and flat [Fe/H] gradients, and tentative indications of positive [Mg/H] and [Mg/Fe] gradients. These results suggest that galaxy cores are older and perhaps also Mg deficient compared to galaxy outskirts. The age gradients may indicate inside-out quenching, while Mg-deficient cores could suggest rapid gas expulsion as the central quenching mechanism. Thus, galaxy cores may have formed faster and quenched more efficiently than their outskirts. However, our [Fe/H] and [Mg/Fe] gradients are still puzzling, and further investigation is required to understand the nature of [Mg/H] gradients in massive galaxies at these redshifts. Our results contrast with those of lower-$z$ studies, which find flat age and [Mg/Fe] gradients and negative metallicity gradients. Additionally, we find a positive trend between age gradients and rotational support and marginal trends between [Fe/H] gradients and velocity dispersions and ages. We discuss our findings in the context of galaxy growth scenarios, including minor mergers and progenitor bias. With this work, we present the first stellar population gradients from NIRSpec-MSA spectroscopy in the current largest sample of distant quiescent galaxies.

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 derives age, [Fe/H], [Mg/H], and [Mg/Fe] gradients for eight massive quiescent galaxies at 1.2 ≲ z ≲ 2.2 using full-spectrum fitting to ultra-deep JWST NIRSpec-MSA spectra from the SUSPENSE survey. It reports negative age gradients, flat [Fe/H] gradients, and tentative positive [Mg/H] and [Mg/Fe] gradients, interpreting these as evidence that galaxy cores are older and Mg-deficient relative to outskirts, consistent with inside-out quenching. The results contrast with lower-redshift studies and are discussed in the context of minor mergers, progenitor bias, and a positive trend between age gradients and rotational support.

Significance. If the gradients prove robust, this provides the first spectroscopic stellar population gradients from NIRSpec-MSA for a sample of distant quiescent galaxies, offering new constraints on quenching and assembly at cosmic noon. The contrast with local trends and the link to rotational support are valuable for distinguishing formation scenarios. The use of ultra-deep spectroscopy on a statistically useful sample of eight galaxies is a clear strength.

major comments (2)
  1. [Methods (spectral fitting)] Methods section (fitting procedure): No mock-recovery tests or simulated spectra are reported to quantify biases in recovered age and abundance gradients at the observed S/N, wavelength coverage, and radial binning. This is load-bearing for the central claim because the negative age gradients and tentative [Mg/Fe] trends rest on the assumption that the SSP templates, dust parameterization, and IMF assumptions do not introduce systematic offsets that alias into the solutions.
  2. [Results] Results section: Gradients are presented without per-galaxy uncertainties or error bars, and no explicit tests of model systematics (template libraries, dust, IMF) are shown. This undermines assessment of the statistical significance of the reported positive trend between age gradients and rotational support and the tentative [Mg/H] and [Mg/Fe] trends.
minor comments (2)
  1. [Abstract] Abstract: The phrasing 'perhaps also Mg deficient' is vague; tie it explicitly to the measured [Mg/H] and [Mg/Fe] gradient values and their uncertainties.
  2. [Figures and Methods] Figure captions and text: Add more detail on how radial bins were chosen and the S/N thresholds applied, as these are listed among the free parameters.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We are pleased that the referee recognizes the significance of our results on stellar population gradients in high-redshift quiescent galaxies. Below we address the major comments point by point, and we will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods (spectral fitting)] Methods section (fitting procedure): No mock-recovery tests or simulated spectra are reported to quantify biases in recovered age and abundance gradients at the observed S/N, wavelength coverage, and radial binning. This is load-bearing for the central claim because the negative age gradients and tentative [Mg/Fe] trends rest on the assumption that the SSP templates, dust parameterization, and IMF assumptions do not introduce systematic offsets that alias into the solutions.

    Authors: We agree that quantifying potential biases through mock-recovery tests is crucial for validating our gradient measurements. Although our spectral fitting methodology is based on established techniques and has been cross-checked with multiple indicators in the current analysis, we did not include explicit mock tests in the submitted manuscript. In the revised version, we will add a dedicated subsection to the Methods section describing mock-recovery experiments. These will use simulated spectra generated with known input age and abundance gradients, convolved with the observed S/N, wavelength coverage, and radial binning from the SUSPENSE data. This will allow us to assess any systematic offsets arising from SSP templates, dust parameterization, or IMF assumptions, directly addressing the robustness of the negative age gradients and [Mg/Fe] trends. revision: yes

  2. Referee: [Results] Results section: Gradients are presented without per-galaxy uncertainties or error bars, and no explicit tests of model systematics (template libraries, dust, IMF) are shown. This undermines assessment of the statistical significance of the reported positive trend between age gradients and rotational support and the tentative [Mg/H] and [Mg/Fe] trends.

    Authors: We acknowledge that presenting per-galaxy uncertainties and systematic tests would enhance the interpretability of our results. The original manuscript reports the gradients but does not include individual error bars for each galaxy or comprehensive model variation tests. We will revise the Results section to include per-galaxy uncertainties on the age, [Fe/H], [Mg/H], and [Mg/Fe] gradients, obtained from the fitting posteriors. Furthermore, we will add explicit tests of model systematics by varying the template libraries, dust attenuation parameters, and IMF assumptions, and present the resulting variations in the gradients. These additions will support the assessment of the positive trend between age gradients and rotational support, as well as the tentative [Mg/H] and [Mg/Fe] trends, by demonstrating their robustness against modeling choices. revision: yes

Circularity Check

0 steps flagged

Direct spectral fitting produces gradients as measurements, not derivations reducing to inputs

full rationale

The paper obtains its central results (negative age gradients, flat [Fe/H] gradients, tentative positive [Mg/H] and [Mg/Fe] gradients) by applying full-spectrum fitting directly to the observed NIRSpec-MSA spectra of the eight galaxies. This is an empirical extraction step from data rather than a theoretical chain that reduces to fitted parameters by construction or relies on self-citations for uniqueness. The abstract explicitly contrasts the findings with lower-redshift studies as an external benchmark, and no load-bearing steps invoke self-definitional relations, renamed predictions, or ansatzes imported via prior author work. The derivation remains self-contained against the input spectra and fitting procedure.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The analysis relies on standard stellar population synthesis assumptions and the validity of full-spectrum fitting to break degeneracies; no new particles or forces are introduced.

free parameters (1)
  • radial binning and S/N thresholds
    Choices of how to extract spectra in radial bins and minimum signal-to-noise for fitting are set by the authors to enable the gradient measurements.
axioms (1)
  • domain assumption Stellar population synthesis models accurately predict observed spectra across the relevant age and metallicity range without unaccounted systematics
    Invoked when fitting full-spectrum models to derive ages and abundances from NIRSpec data.

pith-pipeline@v0.9.0 · 5998 in / 1467 out tokens · 33433 ms · 2026-05-18T15:51:23.492504+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

140 extracted references · 140 canonical work pages · 1 internal anchor

  1. [1]

    E., Leja, J., et al

    Akhshik, M., Whitaker, K. E., Leja, J., et al. 2023, ApJ, 943, 179

    work page 2023

  2. [2]

    2016, Empirical Models for the WFC3/IR PSF, Instrument Science Report WFC3 2016-12, 42 pages

    Anderson, J. 2016, Empirical Models for the WFC3/IR PSF, Instrument Science Report WFC3 2016-12, 42 pages

    work page 2016

  3. [3]

    H., Weinberg, D

    Andrews, B. H., Weinberg, D. H., Schönrich, R., & Johnson, J. A. 2017, ApJ, 835, 224 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

    work page 2017

  4. [4]

    M., Koo, D

    Barro, G., Faber, S. M., Koo, D. C., et al. 2017, ApJ, 840, 47

    work page 2017

  5. [5]

    M., Pérez-González, P

    Barro, G., Faber, S. M., Pérez-González, P. G., et al. 2013, ApJ, 765, 104

    work page 2013

  6. [6]

    Bell, E. F. & de Jong, R. S. 2001, ApJ, 550, 212

    work page 2001

  7. [7]

    L., et al

    Belli, S., Park, M., Davies, R. L., et al. 2024, Nature, 630, 54

    work page 2024

  8. [8]

    2024, alizabeverage/alfalpha: Initial Release alongside Beverage et al

    Beverage, A. 2024, alizabeverage/alfalpha: Initial Release alongside Beverage et al. 2024

    work page 2024

  9. [9]

    G., Kriek, M., Conroy, C., et al

    Beverage, A. G., Kriek, M., Conroy, C., et al. 2021, ApJ, 917, L1

    work page 2021

  10. [10]

    G., Kriek, M., Conroy, C., et al

    Beverage, A. G., Kriek, M., Conroy, C., et al. 2023, ApJ, 948, 140

    work page 2023

  11. [11]

    G., Kriek, M., Suess, K

    Beverage, A. G., Kriek, M., Suess, K. A., et al. 2024, ApJ, 966, 234

    work page 2024

  12. [12]

    G., Slob, M., Kriek, M., et al

    Beverage, A. G., Slob, M., Kriek, M., et al. 2025, ApJ, 979, 249

    work page 2025

  13. [13]

    G., Tal, T., et al

    Bezanson, R., van Dokkum, P. G., Tal, T., et al. 2009, ApJ, 697, 1290

    work page 2009

  14. [14]

    Bluck, A. F. L., Mendel, J. T., Ellison, S. L., et al. 2014, MNRAS, 441, 599

    work page 2014

  15. [15]

    G., Benson, A

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

    work page 2006

  16. [16]

    2024, astropy/photutils: 1.12.0

    Bradley, L., Sip˝ocz, B., Robitaille, T., et al. 2024, astropy/photutils: 1.12.0

    work page 2024

  17. [17]

    Stellar population synthesis at the resolution of 2003

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

    work page 2003

  18. [18]

    2023, JWST Calibration Pipeline

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2023, JWST Calibration Pipeline

    work page 2023

  19. [19]

    Carnall, A. C. 2017, arXiv e-prints, arXiv:1705.05165

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  20. [20]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., et al. 2022, ApJ, 929, 131

    work page 2022

  21. [21]

    M., Bschorr, T

    Carollo, C. M., Bschorr, T. J., Renzini, A., et al. 2013, ApJ, 773, 112

    work page 2013

  22. [22]

    Tracing High-<i>z</i> Galaxies in X-Rays with JWST and Chandra

    Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31

    work page 2023

  23. [23]

    M., Kriek, M., Beverage, A

    Cheng, C. M., Kriek, M., Beverage, A. G., et al. 2024, MNRAS, 532, 3604

    work page 2024

  24. [24]

    2016, ApJ, 823, 102

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102

    work page 2016

  25. [25]

    2017, MNRAS, 466, 4492

    Ciocca, F., Saracco, P., Gargiulo, A., & De Propris, R. 2017, MNRAS, 466, 4492

    work page 2017

  26. [26]

    & van Dokkum, P

    Conroy, C. & van Dokkum, P. 2012, ApJ, 747, 69

    work page 2012

  27. [27]

    G., & Lind, K

    Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, ApJ, 854, 139

    work page 2018

  28. [28]

    J., Springel, V ., White, S

    Croton, D. J., Springel, V ., White, S. D. M., et al. 2006, MNRAS, 365, 11

    work page 2006

  29. [29]

    J., Utsumi, Y ., & Dell’Antonio, I

    Damjanov, I., Sohn, J., Geller, M. J., Utsumi, Y ., & Dell’Antonio, I. 2023, ApJ, 943, 149

    work page 2023

  30. [30]

    J., Geller, M

    Damjanov, I., Zahid, H. J., Geller, M. J., et al. 2019, ApJ, 872, 91 de Graaff, A., Rix, H.-W., Carniani, S., et al. 2024, A&A, 684, A87 De Lucia, G., Fontanot, F., & Hirschmann, M. 2017, MNRAS, 466, L88

    work page 2019

  31. [31]

    & Birnboim, Y

    Dekel, A. & Birnboim, Y . 2006, MNRAS, 368, 2

    work page 2006

  32. [32]

    & Burkert, A

    Dekel, A. & Burkert, A. 2014, MNRAS, 438, 1870 D’Eugenio, F., Pérez-González, P. G., Maiolino, R., et al. 2024, Nature Astron- omy, 8, 1443 D’Eugenio, F., van der Wel, A., Wu, P.-F., et al. 2020, MNRAS, 497, 389 Article number, page 12 of 15 Chloe M. Cheng et al.:JWST-SUSPENSE gradients Di Matteo, T., Springel, V ., & Hernquist, L. 2005, Nature, 433, 604

    work page 2014

  33. [33]

    R., Andreon, S., Longhetti, M., & Newman, A

    Ditrani, F. R., Andreon, S., Longhetti, M., & Newman, A. 2022, A&A, 660, A132

    work page 2022

  34. [34]

    2007, wx2d: A PyRAF Routine to Resample Spectral Images, Instrument Science Report STIS 2007-04, 20 pages

    Dressel, L., Hodge, P., & Barrett, P. 2007, wx2d: A PyRAF Routine to Resample Spectral Images, Instrument Science Report STIS 2007-04, 20 pages

    work page 2007

  35. [35]

    C., et al

    Dumont, A., Neumayer, N., Seth, A. C., et al. 2025, arXiv e-prints, arXiv:2503.09697

    work page arXiv 2025

  36. [36]

    L., Sánchez, S

    Ellison, S. L., Sánchez, S. F., Ibarra-Medel, H., et al. 2018, MNRAS, 474, 2039

    work page 2018

  37. [37]

    2019, MNRAS, 489, 608

    Ferreras, I., Scott, N., La Barbera, F., et al. 2019, MNRAS, 489, 608

    work page 2019

  38. [38]

    2022, A&A, 661, A81

    Ferruit, P., Jakobsen, P., Giardino, G., et al. 2022, A&A, 661, A81

    work page 2022

  39. [39]

    2017, MNRAS, 464, 3812

    Fontanot, F., De Lucia, G., Hirschmann, M., et al. 2017, MNRAS, 464, 3812

    work page 2017

  40. [40]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306

    work page 2013

  41. [41]

    & Illingworth, G

    Franx, M. & Illingworth, G. 1990, ApJ, 359, L41

    work page 1990

  42. [42]

    G., Förster Schreiber, N

    Franx, M., van Dokkum, P. G., Förster Schreiber, N. M., et al. 2008, ApJ, 688, 770

    work page 2008

  43. [43]

    Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41

    work page 2005

  44. [44]

    2017, MNRAS, 465, 688 González Delgado, R

    Goddard, D., Thomas, D., Maraston, C., et al. 2017, MNRAS, 465, 688 González Delgado, R. M., García-Benito, R., Pérez, E., et al. 2015, A&A, 581, A103

    work page 2017

  45. [45]

    E., Janish, R., Ma, C.-P., et al

    Greene, J. E., Janish, R., Ma, C.-P., et al. 2015, ApJ, 807, 11

    work page 2015

  46. [46]

    E., Murphy, J

    Greene, J. E., Murphy, J. D., Graves, G. J., et al. 2013, ApJ, 776, 64

    work page 2013

  47. [47]

    E., Veale, M., Ma, C.-P., et al

    Greene, J. E., Veale, M., Ma, C.-P., et al. 2019, ApJ, 874, 66

    work page 2019

  48. [48]

    L., Cooper, M

    Griffith, R. L., Cooper, M. C., Newman, J. A., et al. 2012, ApJS, 200, 9

    work page 2012

  49. [49]

    E., Newman, A

    Gu, M., Greene, J. E., Newman, A. B., et al. 2022, ApJ, 932, 103

    work page 2022

  50. [50]

    F., Cox, T

    Hopkins, P. F., Cox, T. J., Dutta, S. N., et al. 2009, ApJS, 181, 135

    work page 2009

  51. [51]

    F., Cox, T

    Hopkins, P. F., Cox, T. J., Kereš, D., & Hernquist, L. 2008, ApJS, 175, 390

    work page 2008

  52. [52]

    F., Croton, D., Bundy, K., et al

    Hopkins, P. F., Croton, D., Bundy, K., et al. 2010, ApJ, 724, 915

    work page 2010

  53. [53]

    B., Mobasher, B., et al

    Jafariyazani, M., Newman, A. B., Mobasher, B., et al. 2025, ApJ, 986, 148

    work page 2025

  54. [54]

    B., Mobasher, B., et al

    Jafariyazani, M., Newman, A. B., Mobasher, B., et al. 2020, ApJ, 897, L42

    work page 2020

  55. [55]

    B., et al

    Jones, T., Wang, X., Schmidt, K. B., et al. 2015, AJ, 149, 107

    work page 2015

  56. [56]

    2025, ApJ, 978, L39

    Ju, M., Wang, X., Jones, T., et al. 2025, ApJ, 978, L39

    work page 2025

  57. [57]

    K., Abraham, R

    Keating, S. K., Abraham, R. G., Schiavon, R., et al. 2015, ApJ, 798, 26

    work page 2015

  58. [58]

    2004, MNRAS, 347, 740

    Kobayashi, C. 2004, MNRAS, 347, 740

    work page 2004

  59. [59]

    M., Aussel, H., Calzetti, D., et al

    Koekemoer, A. M., Aussel, H., Calzetti, D., et al. 2007, ApJS, 172, 196

    work page 2007

  60. [60]

    G., et al

    Kriek, M., Conroy, C., van Dokkum, P. G., et al. 2016, Nature, 540, 248

    work page 2016

  61. [61]

    H., Conroy, C., et al

    Kriek, M., Price, S. H., Conroy, C., et al. 2019, ApJ, 880, L31

    work page 2019

  62. [62]

    2001, MNRAS, 322, 231

    Kroupa, P. 2001, MNRAS, 322, 231

    work page 2001

  63. [63]

    2006, MNRAS, 369, 497

    Kuntschner, H., Emsellem, E., Bacon, R., et al. 2006, MNRAS, 369, 497

    work page 2006

  64. [64]

    2010, MNRAS, 408, 97 La Barbera, F., de Carvalho, R

    Kuntschner, H., Emsellem, E., Bacon, R., et al. 2010, MNRAS, 408, 97 La Barbera, F., de Carvalho, R. R., Gal, R. R., et al. 2005, ApJ, 626, L19

    work page 2010

  65. [65]

    Larson, R. B. 1974, MNRAS, 166, 585

    work page 1974

  66. [66]

    Law, D. R., E. Morrison, J., Argyriou, I., et al. 2023, AJ, 166, 45

    work page 2023

  67. [67]

    C., Johnson, B

    Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019, ApJ, 876, 3

    work page 2019

  68. [68]

    2018, MNRAS, 476, 1765

    Li, H., Mao, S., Cappellari, M., et al. 2018, MNRAS, 476, 1765

    work page 2018

  69. [69]

    R., Fekete, G., et al

    Lupton, R., Blanton, M. R., Fekete, G., et al. 2004, PASP, 116, 133

    work page 2004

  70. [70]

    & Mannucci, F

    Maiolino, R. & Mannucci, F. 2019, A&A Rev., 27, 3

    work page 2019

  71. [71]

    W., Feiden, G

    Mann, A. W., Feiden, G. A., Gaidos, E., Boyajian, T., & von Braun, K. 2015, ApJ, 804, 64

    work page 2015

  72. [72]

    2009, ApJ, 707, 250 Martín-Navarro, I

    Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250 Martín-Navarro, I. 2016, MNRAS, 456, L104 Martín-Navarro, I., Vazdekis, A., Falcón-Barroso, J., et al. 2018, MNRAS, 475, 3700

    work page 2009

  73. [73]

    V ., Lewis, Z., Matthee, J., et al

    Maseda, M. V ., Lewis, Z., Matthee, J., et al. 2023, ApJ, 956, 11

    work page 2023

  74. [74]

    1994, A&A, 288, 57

    Matteucci, F. 1994, A&A, 288, 57

    work page 1994

  75. [75]

    J., Milvang-Jensen, B., Dunlop, J., et al

    McCracken, H. J., Milvang-Jensen, B., Dunlop, J., et al. 2012, A&A, 544, A156

    work page 2012

  76. [76]

    P., Bender, R., & Wegner, G

    Mehlert, D., Thomas, D., Saglia, R. P., Bender, R., & Wegner, G. 2003, A&A, 407, 423

    work page 2003

  77. [77]

    B., van Dokkum, P., & Mowla, L

    Miller, T. B., van Dokkum, P., & Mowla, L. 2023, ApJ, 945, 155

    work page 2023

  78. [78]

    B., Whitaker, K

    Miller, T. B., Whitaker, K. E., Nelson, E. J., et al. 2022, ApJ, 941, L37

    work page 2022

  79. [79]

    Moffat, A. F. J. 1969, A&A, 3, 455

    work page 1969

  80. [80]

    H., & Ostriker, J

    Naab, T., Johansson, P. H., & Ostriker, J. P. 2009, ApJ, 699, L178

    work page 2009

Showing first 80 references.