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arxiv: 2604.05680 · v1 · submitted 2026-04-07 · 🌌 astro-ph.GA

Recognition: 2 theorem links

· Lean Theorem

Stellar Population Characterisations in nearby, dusty Early-Type Galaxies

Ron W. Savage , Anne E. Sansom , David H. W. Glass
Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:38 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords dusty early-type galaxiesstellar populationsstar formationfull spectrum fittingLick indicesgalaxy kinematicsspectroscopy
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The pith

Dusty early-type galaxies often contain young stellar populations suggesting recent star formation.

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

This paper examines optical spectra along the major axis of 15 nearby dusty early-type galaxies selected from surveys for their high dust content. Full spectrum fitting and Lick index methods applied to sMILES and MILES stellar population models reveal young or intermediate-age components in 12 of the 15 galaxies, with ages around 1 Gyr. Simulations confirm these components are not produced by the fitting process or data limitations. Stacked SDSS spectra of dusty ETGs show an intermediate-age feature absent in non-dusty ETGs. Most galaxies appear rotationally supported with no kinematic breaks, and standard trends of increasing age, metallicity, and alpha abundance with velocity dispersion appear but with greater scatter.

Core claim

Twelve of fifteen dusty early-type galaxies display young or intermediate-age stellar population components indicating ongoing or recent star formation. These approximately 1 Gyr populations are verified as real through simulations rather than fitting artifacts. The sample galaxies are mostly rotationally supported without detectable kinematic discontinuities. Stacked SDSS spectra confirm the presence of intermediate-age components only in dusty ETGs and not in non-dusty counterparts. Age, metallicity, and alpha-element abundance ratios increase with central velocity dispersion, though with larger scatter than in prior ETG studies.

What carries the argument

Full spectrum fitting and Lick index fitting using the sMILES and MILES stellar population libraries applied to long-slit spectra to extract ages, metallicities, alpha abundances, and kinematics within the effective radius.

If this is right

  • Recent star formation may supply or result from the observed high dust and molecular gas content through internal processes or external accretion.
  • Kinematic continuity implies that any dust-acquiring events did not strongly disrupt the rotational support of these galaxies.
  • The increased scatter in age-metallicity-velocity dispersion relations may reflect the influence of recent star formation episodes mixed with older populations.
  • Formation models must account for both dust retention and the observed young stellar components to explain the properties of these ETGs.

Where Pith is reading between the lines

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

  • Dust levels could be used as a selection criterion to identify ETGs with recent star formation in larger surveys.
  • Multi-wavelength data might help isolate dust reddening effects from true age signals in future analyses.
  • Comparing these findings to simulations of galaxy mergers or gas inflows could test specific dust origin scenarios.

Load-bearing premise

That full spectrum fitting and Lick index methods with sMILES and MILES libraries can reliably separate young stellar components from older populations despite the presence of dust and without significant systematic biases in the selected sample.

What would settle it

Independent age dating of a larger sample of dusty ETGs using UV photometry or resolved stellar populations that finds no young components in most cases.

Figures

Figures reproduced from arXiv: 2604.05680 by Anne E. Sansom, David H. W. Glass, Ron W. Savage.

Figure 1
Figure 1. Figure 1: H𝛼 emission line equivalent width vs log10([NII]𝜆6584/H𝛼) emis￾sion line ratio WHAN diagram (Cid Fernandes et al. 2010, 2011), with the selected dusty ETGs highlighted. For reference, the grey points show our Initial Complete Sample (see Section 2) of galaxies with ≥3𝜎 detection of relevant emission lines. Emission line strengths and equivalent widths were sourced from GAMA II GaussFitSimplev05. The vertic… view at source ↗
Figure 2
Figure 2. Figure 2: The top panel plots dust mass (𝑀𝑑) vs. stellar mass (𝑀∗) showing the relatively high dust content of the 15 selected ETGs compared with the 608 E type (purple dots) and 461 S0 type (orange dots) galaxies in the Parent Sample. The lower panel plots star formation rate (SFR) vs. 𝑀∗. The grey cloud of dots shows the overall Parent Sample of 4458 galaxies and the black line represents the Galaxy Main Sequence … view at source ↗
Figure 3
Figure 3. Figure 3: Plot of 𝜎0 for the central 𝑅𝑒/8 aperture of each target ETG, showing that 𝜎0 results from pPXF fittings using SSPs from the MILES and sMILES libraries are within 1-sigma uncertainties for each target ETG. 4.1 Stellar kinematics and profiles The line-of-sight recession velocity (V) and central velocity disper￾sion (𝜎0) were derived from the central 𝑅𝑒/8 aperture spectrum of each target, using pPXF (Cappella… view at source ↗
Figure 4
Figure 4. Figure 4: Example profiles of rotational velocity (V𝑅𝑜𝑡), line of sight velocity dispersion (𝜎), Age𝐿 and [M/H]𝐿 along the major axis of GAMA272990, with the x axis showing position relative to the galaxy centre. Results from fitting with MILES SSPs are shown in blue, results obtained using sMILES SSPs with [𝛼/Fe] = 0.0 are shown in red. Vertical orange lines show the spatial centre, 𝑅𝑒/8 and 𝑅𝑒. V𝑅𝑜𝑡 is the line of… view at source ↗
Figure 5
Figure 5. Figure 5: Example outputs from luminosity weighted pPXF full spectrum fitting for GAMA422436 (left panels) and GAMA99687 (right panels). For each target the upper plot shows relative flux vs. wavelength with the observed spectrum in black, the best fit stellar spectrum generated by combining template SSPs in red, the gas emission fit in magenta, fitting residuals in green and masked areas in grey. The lower plots sh… view at source ↗
Figure 6
Figure 6. Figure 6: Upper panels show Age, [M/H] and [𝛼/Fe] from pPXF full spectrum fitting and Lick index SSP fitting of the central 𝑅𝑒/8 apertures versus 𝜎0. Results are shown as points with 1-sigma error bars, for 13 dusty ETGs, excluding the highest H𝛼 emitters GAMA298980 and GAMA569555. Trend lines show Tukey biweight regression lines with a cut-off parameter of c=3.5 fitted to the points. GAMA85416 is shown with open sy… view at source ↗
Figure 7
Figure 7. Figure 7: Example diagnostic plots from Lick index SSP fitting of GAMA422436. The upper panel shows fitting results from each of two Monte Carlo simulations of 100 cycles using a perturbed input spectrum. Blue data were generated from fits starting at 1 Gyr, red data from fits starting at an 7 Gyr. The lower panel shows Age vs. metallicity and metallicity vs. [𝛼/Fe] contour plots of normalised reduced 𝜒 2 across the… view at source ↗
Figure 8
Figure 8. Figure 8: Lick indices SSP fits to SDSS stacks for non-dusty ETG (2 left panels) and dusty ETGs (2 right panels). See description of contours in [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Top: Luminosity-weighted pPXF fits to SDSS stacked spectra for non-dusty ETGs (left panels) and dusty ETGs (right panels). Middle: Grid showing SSPs fitting these spectra (linearly scaled with default pPXF colour palette). See description in [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
read the original abstract

Dust in Early-Type galaxies (ETGs) may originate from internal or external sources. In this paper we study the stellar populations of particularly dusty ETGs to search for evidence of the dust's origin. Using the Southern African Large Telescope (SALT), we obtained long-slit optical spectra within the effective radius (R_e), along the major axis of 15 nearby ETGs, selected from the GAMA and Herschel-ATLAS surveys for their high levels of interstellar dust. Using full spectrum fitting and Lick index fitting we analysed their major axis kinematics and stellar population characteristics. We used stellar population models from the newly developed sMILES library and from the empirical MILES library. Kinematic results show that most of our sample of dusty ETGs are rotationally supported and there are no detectable kinematic discontinuities. 12 of our sample of 15 dusty ETGs show evidence of young/intermediate age stellar population components suggesting ongoing/recent star formation. Using simulations, we show that these recent ($\approx$1~Gyr) populations are not artefacts of the fitting process or data. As a check with a control sample we use stacked SDSS spectra and find that dusty ETGs show a component with intermediate age, whereas non-dusty ETGs do not. Age, metallicity and $\alpha$-element abundance ratio increase with increasing central velocity dispersion in the SALT spectra, as seen in previous studies of ETGs, but with larger scatter in our sample. Given our stellar population findings, we discuss formation scenarios that might cause or rule out a high dust/molecular gas content.

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 / 3 minor

Summary. The manuscript presents long-slit optical spectra of 15 nearby dusty ETGs selected from GAMA and Herschel-ATLAS, observed with SALT along the major axis within R_e. Full-spectrum fitting with the sMILES library and Lick-index analysis with MILES are used to derive kinematics and stellar population parameters (age, metallicity, alpha-enhancement). The central result is that 12/15 galaxies exhibit young-to-intermediate age (~1 Gyr) components, interpreted as evidence of recent star formation; simulations are cited to rule out fitting artefacts, a stacked SDSS control sample shows intermediate-age populations only in dusty ETGs, and scaling relations with central velocity dispersion follow literature trends but exhibit larger scatter.

Significance. If the young stellar components are robustly separated from dust effects, the result would constrain dust origins in ETGs (internal from recent SF versus external) and support formation scenarios involving gas accretion or minor mergers. Credit is due for the use of the new sMILES library, the inclusion of simulations to test fitting artefacts, the independent SDSS control sample, and the kinematic continuity check. The larger scatter in scaling relations, if quantified, could also highlight sample-specific effects.

major comments (2)
  1. [Spectral fitting and stellar population analysis] Spectral fitting procedure: the description does not specify whether dust attenuation (e.g., a Calzetti screen or similar) is included as a free parameter fitted simultaneously with the stellar templates or applied only post-hoc. This is load-bearing for the headline claim, because differential reddening along the slit can steepen the continuum and mimic a young population while leaving metal lines relatively unchanged—the exact degeneracy the simulations are intended to address.
  2. [Simulations validating young populations] Validation simulations: although the abstract states that simulations demonstrate the ~1 Gyr populations are not artefacts of the fitting process or data, no details are given on the simulation design (e.g., whether realistic dust geometries, attenuation curves, or the precise age-metallicity-extinction grid used in the real fits are included). Without this, it is not possible to confirm that the simulations adequately test the dust-age separation central to the result.
minor comments (3)
  1. [Scaling relations with velocity dispersion] The statement that scaling relations show 'larger scatter' is not supported by any quantitative metric (rms, standard deviation, or statistical comparison to literature samples) or reference to a specific table or figure.
  2. [SDSS control sample] The control-sample comparison would benefit from explicit details on how the SDSS stacks were constructed (mass matching, dust selection criteria, number of galaxies per stack) to allow assessment of whether the absence of intermediate-age components in non-dusty ETGs is statistically robust.
  3. [Abstract] Minor typographical inconsistencies appear in the abstract (e.g., inconsistent use of '≈1 Gyr' versus 'recent (≈1 Gyr) populations') and should be harmonized with the main text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. We address the two major comments below and will revise the paper to provide the requested clarifications and details.

read point-by-point responses

  1. Referee: [Spectral fitting and stellar population analysis] Spectral fitting procedure: the description does not specify whether dust attenuation (e.g., a Calzetti screen or similar) is included as a free parameter fitted simultaneously with the stellar templates or applied only post-hoc. This is load-bearing for the headline claim, because differential reddening along the slit can steepen the continuum and mimic a young population while leaving metal lines relatively unchanged—the exact degeneracy the simulations are intended to address.

    Authors: We agree that the manuscript does not provide a sufficiently explicit description of how dust attenuation is treated during the full-spectrum fitting with the sMILES library. We will revise the methods section to state clearly whether dust attenuation was included as a free parameter in the fits or handled separately (e.g., post-hoc or via the Lick-index analysis), and we will add a discussion of the implications for continuum shape and the age-dust degeneracy. This will directly address the concern and strengthen the interpretation of the young stellar components. revision: yes

  2. Referee: [Simulations validating young populations] Validation simulations: although the abstract states that simulations demonstrate the ~1 Gyr populations are not artefacts of the fitting process or data, no details are given on the simulation design (e.g., whether realistic dust geometries, attenuation curves, or the precise age-metallicity-extinction grid used in the real fits are included). Without this, it is not possible to confirm that the simulations adequately test the dust-age separation central to the result.

    Authors: We accept that the current description of the validation simulations lacks the necessary detail on their design. We will expand the relevant section (and any associated appendix) to fully specify the simulation setup, including the age-metallicity grid, noise model, whether dust attenuation curves or geometries were incorporated, and how the mock spectra were fitted using the same procedure as the real data. These additions will allow readers to evaluate the robustness of the ~1 Gyr population detections against fitting artefacts and dust-related degeneracies. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results from external model fits and control sample

full rationale

The paper derives its central claim (young/intermediate-age components in 12/15 dusty ETGs) from direct long-slit spectra fitted via full-spectrum and Lick-index methods to independent external libraries (sMILES and MILES). Simulations serve as an independent validation against artefacts, while the SDSS stacked control sample provides external contrast showing the intermediate-age component is absent in non-dusty ETGs. Kinematic continuity and velocity-dispersion trends are reported as observed patterns without reduction to self-defined quantities. No self-citations, ansatzes, or fitted inputs are invoked as load-bearing for the result; the derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim depends on the accuracy of stellar population synthesis models and the assumption that spectral fitting can isolate age components in dusty systems; no new entities are postulated.

free parameters (1)
  • multiple age and metallicity components in spectral fitting
    Fitting routines typically optimize or select discrete age bins and metallicities to match observed spectra, introducing fitted values that define the reported young populations.
axioms (2)
  • domain assumption sMILES and MILES stellar population libraries provide unbiased representations of the spectra of dusty ETGs
    Analysis relies on these models to derive ages and abundances without detailed justification of their applicability to dusty environments.
  • domain assumption Dust extinction does not systematically bias the detection of young stellar components in the fitting process
    The paper invokes simulations to address artifacts but treats dust effects as manageable within the chosen methods.

pith-pipeline@v0.9.0 · 5593 in / 1553 out tokens · 54004 ms · 2026-05-10T18:38:41.169350+00:00 · methodology

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Reference graph

Works this paper leans on

102 extracted references · 96 canonical work pages · 2 internal anchors

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    K., et al., 2013, @doi [ ] 10.1093/mnras/stt310 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.431.1929A 431, 1929

    Agius N. K., et al., 2013, @doi [ ] 10.1093/mnras/stt310 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.431.1929A 431, 1929

    work page doi:10.1093/mnras/stt310 2013

  3. [3]

    K., et al., 2015, @doi [ ] 10.1093/mnras/stv1191 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.3815A 451, 3815

    Agius N. K., et al., 2015, @doi [ ] 10.1093/mnras/stv1191 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.3815A 451, 3815

    work page doi:10.1093/mnras/stv1191 2015

  4. [4]

    Bassett R., et al., 2017, @doi [ ] 10.1093/mnras/stx1000 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.1991B 470, 1991

    work page doi:10.1093/mnras/stx1000 2017

  5. [5]

    F., et al., 2017, @doi [ ] 10.1093/mnras/stx1835 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4005B 471, 4005

    Boardman N. F., et al., 2017, @doi [ ] 10.1093/mnras/stx1835 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4005B 471, 4005

    work page doi:10.1093/mnras/stx1835 2017

  6. [6]

    J., Combes F., 2005, @doi [ ] 10.1051/0004-6361:20042036 , https://ui.adsabs.harvard.edu/abs/2005A&A...437...69B 437, 69

    Bournaud F., Jog C. J., Combes F., 2005, @doi [ ] 10.1051/0004-6361:20042036 , https://ui.adsabs.harvard.edu/abs/2005A&A...437...69B 437, 69

    work page doi:10.1051/0004-6361:20042036 2005

  7. [7]

    J., Combes F., 2007, @doi [ ] 10.1051/0004-6361:20078010 , https://ui.adsabs.harvard.edu/abs/2007A&A...476.1179B 476, 1179

    Bournaud F., Jog C. J., Combes F., 2007, @doi [ ] 10.1051/0004-6361:20078010 , https://ui.adsabs.harvard.edu/abs/2007A&A...476.1179B 476, 1179

    work page doi:10.1051/0004-6361:20078010 2007

  8. [8]

    Buckley D. A. H., Swart G. P., Meiring J. G., 2006, in Stepp L. M., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 6267, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. p. 62670Z, @doi 10.1117/12.673750

    work page doi:10.1117/12.673750 2006

  9. [9]

    Buckley D. A. H., et al., 2008, in McLean I. S., Casali M. M., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II. p. 701407, @doi 10.1117/12.789438

    work page doi:10.1117/12.789438 2008

  10. [10]

    Bundy K., et al., 2015, @doi [ ] 10.1088/0004-637X/798/1/7 , https://ui.adsabs.harvard.edu/abs/2015ApJ...798....7B 798, 7

    work page Pith review doi:10.1088/0004-637x/798/1/7 2015

  11. [11]

    Instrument Design and Performance for Optical/Infrared Ground-based Telescopes , year = 2003, editor =

    Burgh E. B., Nordsieck K. H., Kobulnicky H. A., Williams T. B., O'Donoghue D., Smith M. P., Percival J. W., 2003, in Iye M., Moorwood A. F. M., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes. pp 1463--1471, @doi 10.1117/12.460312

    work page doi:10.1117/12.460312 2003

  12. [12]

    Springer Berlin Heidelberg, Berlin, Heidelberg, pp 327--339, @doi 10.1007/978-3-540-45040-5_27 , https://doi.org/10.1007/978-3-540-45040-5_27

    Burkert A., Naab T., 2003, Major Mergers and the Origin of Elliptical Galaxies. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 327--339, @doi 10.1007/978-3-540-45040-5_27 , https://doi.org/10.1007/978-3-540-45040-5_27

    work page doi:10.1007/978-3-540-45040-5_27 2003

  13. [13]

    Cappellari M., 2016, @doi [ ] 10.1146/annurev-astro-082214-122432 , https://ui.adsabs.harvard.edu/abs/2016ARA&A..54..597C 54, 597

    work page doi:10.1146/annurev-astro-082214-122432 2016

  14. [14]

    Cappellari M., 2017, @doi [ ] 10.1093/mnras/stw3020 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..798C 466, 798

    work page internal anchor Pith review doi:10.1093/mnras/stw3020 2017

  15. [15]

    Cappellari M., 2023, @doi [ ] 10.1093/mnras/stad2597 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C 526, 3273

    work page doi:10.1093/mnras/stad2597 2023

  16. [16]

    Cappellari M., Emsellem E., 2004, @doi [ ] 10.1086/381875 , https://ui.adsabs.harvard.edu/abs/2004PASP..116..138C 116, 138

    work page doi:10.1086/381875 2004

  17. [17]

    Cappellari M., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.18174.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413..813C 413, 813

    work page doi:10.1111/j.1365-2966.2010.18174.x 2011

  18. [18]

    Catala L., et al., 2013, @doi [ ] 10.1093/mnras/stt1602 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436..590C 436, 590

    work page doi:10.1093/mnras/stt1602 2013

  19. [19]

    doi:10.1111/j.1365-2966.2009.15598.x , archivePrefix =

    Cid Fernandes R., Stasi \'n ska G., Schlickmann M. S., Mateus A., Vale Asari N., Schoenell W., Sodr \'e L., 2010, @doi [ ] 10.1111/j.1365-2966.2009.16185.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.403.1036C 403, 1036

    work page doi:10.1111/j.1365-2966.2009.16185.x 2010

  20. [20]

    Cid Fernandes R., Stasi \'n ska G., Mateus A., Vale Asari N., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18244.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413.1687C 413, 1687

    work page doi:10.1111/j.1365-2966.2011.18244.x 2011

  21. [21]

    S., et al., 2010, @doi [ ] 10.1051/0004-6361/201014533 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L..50C 518, L50

    Clemens M. S., et al., 2010, @doi [ ] 10.1051/0004-6361/201014533 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L..50C 518, L50

    work page doi:10.1051/0004-6361/201014533 2010

  22. [22]

    Conroy C., 2013, @doi [ ] 10.1146/annurev-astro-082812-141017 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..393C 51, 393

    work page Pith review doi:10.1146/annurev-astro-082812-141017 2013

  23. [23]

    J., van Dokkum P

    Conroy C., Graves G. J., van Dokkum P. G., 2014, @doi [ ] 10.1088/0004-637X/780/1/33 , https://ui.adsabs.harvard.edu/abs/2014ApJ...780...33C 780, 33

    work page doi:10.1088/0004-637x/780/1/33 2014

  24. [24]

    , keywords =

    Conselice C. J., 2014, @doi [ ] 10.1146/annurev-astro-081913-040037 , https://ui.adsabs.harvard.edu/abs/2014ARA&A..52..291C 52, 291

    work page doi:10.1146/annurev-astro-081913-040037 2014

  25. [25]

    Cortese L., et al., 2012, @doi [ ] 10.1051/0004-6361/201118499 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..52C 540, A52

    work page doi:10.1051/0004-6361/201118499 2012

  26. [26]

    7737, Observatory Operations: Strategies, Processes, and Systems III, ed

    Crawford S. M., et al., 2010, in Silva D. R., Peck A. B., Soifer B. T., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7737, Observatory Operations: Strategies, Processes, and Systems III. p. 773725, @doi 10.1117/12.857000

    work page doi:10.1117/12.857000 2010

  27. [27]

    The Merger Rates and Mass Assembly Histories of Dark Matter Haloes in the Two

    Crocker A. F., Bureau M., Young L. M., Combes F., 2011, @doi [ ] 10.1111/j.1365-2966.2010.17537.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.410.1197C 410, 1197

    work page doi:10.1111/j.1365-2966.2010.17537.x 2011

  28. [28]

    I., Nersesian, A., Baes, M., et al

    Davies J. I., et al., 2019, @doi [ ] 10.1051/0004-6361/201935547 , https://ui.adsabs.harvard.edu/abs/2019A&A...626A..63D 626, A63

    work page doi:10.1051/0004-6361/201935547 2019

  29. [29]

    , archivePrefix = "arXiv", eprint =

    Davis T. A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19355.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417..882D 417, 882

    work page doi:10.1111/j.1365-2966.2011.19355.x 2011

  30. [30]

    A., et al., 2015, , 449, 3503

    Davis T. A., et al., 2015, , 449, 3503

    2015

  31. [31]

    T., & Salpeter, E

    Draine B. T., Salpeter E. E., 1979, @doi [ ] 10.1086/157206 , https://ui.adsabs.harvard.edu/abs/1979ApJ...231..438D 231, 438

    work page doi:10.1086/157206 1979

  32. [32]

    P., Norberg, P., Baldry, I

    Driver S. P., et al., 2009, @doi [Astronomy and Geophysics] 10.1111/j.1468-4004.2009.50512.x , https://ui.adsabs.harvard.edu/abs/2009A&G....50e..12D 50, 5.12

    work page doi:10.1111/j.1468-4004.2009.50512.x 2009

  33. [33]

    , keywords =

    Driver S. P., et al., 2022, @doi [ ] 10.1093/mnras/stac472 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513..439D 513, 439

    work page doi:10.1093/mnras/stac472 2022

  34. [34]

    Eales S., et al., 2010, @doi [ ] 10.1086/653086 , https://ui.adsabs.harvard.edu/abs/2010PASP..122..499E 122, 499

    work page doi:10.1086/653086 2010

  35. [35]

    Eales S., et al., 2015, @doi [ ] 10.1093/mnras/stv1300 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.3489E 452, 3489

    work page doi:10.1093/mnras/stv1300 2015

  36. [36]

    J., Gorgas J., Peletier R

    Falc \'o n-Barroso J., S \'a nchez-Bl \'a zquez P., Vazdekis A., Ricciardelli E., Cardiel N., Cenarro A. J., Gorgas J., Peletier R. F., 2011, @doi [ ] 10.1051/0004-6361/201116842 , https://ui.adsabs.harvard.edu/abs/2011A&A...532A..95F 532, A95

    work page doi:10.1051/0004-6361/201116842 2011

  37. [37]

    Ferreras I., et al., 2019, @doi [ ] 10.1093/mnras/stz2095 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489..608F 489, 608

    work page doi:10.1093/mnras/stz2095 2019

  38. [38]

    D., et al., 2023, @doi [ ] 10.3847/1538-4357/aca46e , https://ui.adsabs.harvard.edu/abs/2023ApJ...942...25F 942, 25

    French K. D., et al., 2023, @doi [ ] 10.3847/1538-4357/aca46e , https://ui.adsabs.harvard.edu/abs/2023ApJ...942...25F 942, 25

    work page doi:10.3847/1538-4357/aca46e 2023

  39. [39]

    Gallazzi A., Charlot S., Brinchmann J., White S. D. M., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10548.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.370.1106G 370, 1106

    work page doi:10.1111/j.1365-2966.2006.10548.x 2006

  40. [40]

    Ge J., Yan R., Cappellari M., Mao S., Li H., Lu Y., 2018, @doi [ ] 10.1093/mnras/sty1245 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.2633G 478, 2633

    work page doi:10.1093/mnras/sty1245 2018

  41. [41]

    Glass D. H. W., 2024, Ph D thesis, University of Central Lancashire, Preston, UK, http://doi.org/10.17030/uclan.thesis.00042515

    work page doi:10.17030/uclan.thesis.00042515 2024

  42. [42]

    Glass D. H. W., Sansom A. E., Davis T. A., Popescu C. C., 2022, @doi [ ] 10.1093/mnras/stac3001 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.5524G 517, 5524

    work page doi:10.1093/mnras/stac3001 2022

  43. [43]

    Spatially resolved stellar population properties in galaxies

    Gonz \'a lez Delgado R. M., et al., 2015, @doi [ ] 10.1051/0004-6361/201525938 , https://ui.adsabs.harvard.edu/abs/2015A&A...581A.103G 581, A103

    work page doi:10.1051/0004-6361/201525938 2015

  44. [44]

    Goudfrooij P., de Jong T., 1995, @doi [ ] 10.48550/arXiv.astro-ph/9504011 , https://ui.adsabs.harvard.edu/abs/1995A&A...298..784G 298, 784

    work page doi:10.48550/arxiv.astro-ph/9504011 1995

  45. [45]

    Griffith E., Martini P., Conroy C., 2019, @doi [ ] 10.1093/mnras/sty3405 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484..562G 484, 562

    work page doi:10.1093/mnras/sty3405 2019

  46. [46]

    W., et al., 2024, @doi [ ] 10.1017/pasa.2024.109 , https://ui.adsabs.harvard.edu/abs/2024PASA...41..115H 41, e115

    Holwerda B. W., et al., 2024, @doi [ ] 10.1017/pasa.2024.109 , https://ui.adsabs.harvard.edu/abs/2024PASA...41..115H 41, e115

    work page doi:10.1017/pasa.2024.109 2024

  47. [47]

    J., H \"a u ler B., Nedkova K

    Jegatheesan K., Johnston E. J., H \"a u ler B., Nedkova K. V., 2024, @doi [ ] 10.1051/0004-6361/202347372 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A..32J 684, A32

    work page doi:10.1051/0004-6361/202347372 2024

  48. [48]

    J., H \"a u ler B., Lassen A

    Jegatheesan K., Johnston E. J., H \"a u ler B., Lassen A. E., Riffel R., Chies-Santos A. L., 2025, @doi [ ] 10.1051/0004-6361/202452137 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A.224J 694, A224

    work page doi:10.1051/0004-6361/202452137 2025

  49. [49]

    Johansson J., Thomas D., Maraston C., 2012, @doi [ ] 10.1111/j.1365-2966.2011.20316.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.421.1908J 421, 1908

    work page doi:10.1111/j.1365-2966.2011.20316.x 2012

  50. [50]

    J., et al., 2022, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stac1447 , 514, 6141

    Johnston E. J., et al., 2022, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stac1447 , 514, 6141

    work page doi:10.1093/mnras/stac1447 2022

  51. [51]

    Y., Kniazev A

    Katkov I. Y., Kniazev A. Y., Sil'chenko O. K., 2015, @doi [ ] 10.1088/0004-6256/150/1/24 , https://ui.adsabs.harvard.edu/abs/2015AJ....150...24K 150, 24

    work page doi:10.1088/0004-6256/150/1/24 2015

  52. [52]

    Y., Kniazev A

    Katkov I. Y., Kniazev A. Y., Kasparova A. V., Sil'chenko O. K., 2019, @doi [ ] 10.1093/mnras/sty3268 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.2413K 483, 2413

    work page doi:10.1093/mnras/sty3268 2019

  53. [53]

    Kaviraj S., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20957.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.423...49K 423, 49

    work page doi:10.1111/j.1365-2966.2012.20957.x 2012

  54. [54]

    , archivePrefix = "arXiv", eprint =

    Kelvin L. S., et al., 2014, @doi [ ] 10.1093/mnras/stu1507 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.1647K 444, 1647

    work page doi:10.1093/mnras/stu1507 2014

  55. [55]

    T., Sansom A

    Knowles A. T., Sansom A. E., Allende Prieto C., Vazdekis A., 2021, @doi [ ] 10.1093/mnras/stab1001 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.2286K 504, 2286

    work page doi:10.1093/mnras/stab1001 2021

  56. [56]

    T., Sansom A

    Knowles A. T., Sansom A. E., Vazdekis A., Allende Prieto C., 2023, @doi [ ] 10.1093/mnras/stad1647 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.3450K 523, 3450

    work page doi:10.1093/mnras/stad1647 2023

  57. [57]

    Kokusho T., Kaneda H., Bureau M., Suzuki T., Murata K., Kondo A., Yamagishi M., 2017, @doi [ ] 10.1051/0004-6361/201630158 , https://ui.adsabs.harvard.edu/abs/2017A&A...605A..74K 605, A74

    work page doi:10.1051/0004-6361/201630158 2017

  58. [58]

    Kokusho T., et al., 2019, @doi [ ] 10.1051/0004-6361/201833911 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..87K 622, A87

    work page doi:10.1051/0004-6361/201833911 2019

  59. [59]

    Kuntschner H., et al., 2010, @doi [ ] 10.1111/j.1365-2966.2010.17161.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.408...97K 408, 97

    work page doi:10.1111/j.1365-2966.2010.17161.x 2010

  60. [60]

    G., de Carvalho R

    La Barbera F., Ferreras I., Vazdekis A., de la Rosa I. G., de Carvalho R. R., Trevisan M., Falc \'o n-Barroso J., Ricciardelli E., 2013, @doi [ ] 10.1093/mnras/stt943 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.433.3017L 433, 3017

    work page doi:10.1093/mnras/stt943 2013

  61. [61]

    R., de la Rosa I

    La Barbera F., Pasquali A., Ferreras I., Gallazzi A., de Carvalho R. R., de la Rosa I. G., 2014, @doi [ ] 10.1093/mnras/stu1626 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.1977L 445, 1977

    work page doi:10.1093/mnras/stu1626 2014

  62. [62]

    La Barbera F., Ferreras I., Vazdekis A., 2015, @doi [ ] 10.1093/mnrasl/slv029 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449L.137L 449, L137

    work page doi:10.1093/mnrasl/slv029 2015

  63. [63]

    H., Hwang H

    Lee Y. H., Hwang H. S., Hwang N., Lee J. C., Kim K.-B., 2023, @doi [ ] 10.3847/1538-4357/ace1ea , https://ui.adsabs.harvard.edu/abs/2023ApJ...953...88L 953, 88

    work page doi:10.3847/1538-4357/ace1ea 2023

  64. [64]

    J., Gall C., Hjorth J., Nadolny J., Ryzhov O., Solar M., 2023, @doi [ ] 10.3847/1538-4357/acdcfc , https://ui.adsabs.harvard.edu/abs/2023ApJ...953...27L 953, 27

    Le \'s niewska A., Micha owski M. J., Gall C., Hjorth J., Nadolny J., Ryzhov O., Solar M., 2023, @doi [ ] 10.3847/1538-4357/acdcfc , https://ui.adsabs.harvard.edu/abs/2023ApJ...953...27L 953, 27

    work page doi:10.3847/1538-4357/acdcfc 2023

  65. [65]

    Liske J., et al., 2015, @doi [ ] 10.1093/mnras/stv1436 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.2087L 452, 2087

    work page doi:10.1093/mnras/stv1436 2015

  66. [66]

    Liu Y., 2020, @doi [ ] 10.1093/mnras/staa2012 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.3011L 497, 3011

    work page doi:10.1093/mnras/staa2012 2020

  67. [67]

    W., Zirm A

    Man A. W., Zirm A. W., Toft S., 2016, , 830, 89

    2016

  68. [68]

    Martin G., Kaviraj S., Devriendt J. E. G., Dubois Y., Pichon C., 2018, @doi [ ] 10.1093/mnras/sty1936 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.2266M 480, 2266

    work page doi:10.1093/mnras/sty1936 2018

  69. [69]

    Martini P., Dicken D., Storchi-Bergmann T., 2013, @doi [ ] 10.1088/0004-637X/766/2/121 , https://ui.adsabs.harvard.edu/abs/2013ApJ...766..121M 766, 121

    work page doi:10.1088/0004-637x/766/2/121 2013

  70. [70]

    M., et al., 2015, @doi [ ] 10.1093/mnras/stv105 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.3484M 448, 3484

    McDermid R. M., et al., 2015, @doi [ ] 10.1093/mnras/stv105 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.3484M 448, 3484

    work page doi:10.1093/mnras/stv105 2015

  71. [71]

    J., et al., 2019, @doi [ ] 10.1051/0004-6361/201936055 , https://ui.adsabs.harvard.edu/abs/2019A&A...632A..43M 632, A43

    Micha owski M. J., et al., 2019, @doi [ ] 10.1051/0004-6361/201936055 , https://ui.adsabs.harvard.edu/abs/2019A&A...632A..43M 632, A43

    work page doi:10.1051/0004-6361/201936055 2019

  72. [72]

    J., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1b52 , https://ui.adsabs.harvard.edu/abs/2024ApJ...964..129M 964, 129

    Micha owski M. J., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1b52 , https://ui.adsabs.harvard.edu/abs/2024ApJ...964..129M 964, 129

    work page doi:10.3847/1538-4357/ad1b52 2024

  73. [73]

    J., Driver S

    Moffett A. J., Driver S. P., Lange R., Robotham A., Kelvin L., GAMA Team 2016, in American Astronomical Society Meeting Abstracts \#227. p. 408.01

    2016

  74. [74]

    Pernet E., Boecker A., Mart \' n-Navarro I., 2024, @doi [ ] 10.1051/0004-6361/202449308 , https://ui.adsabs.harvard.edu/abs/2024A&A...687L..14P 687, L14

    work page doi:10.1051/0004-6361/202449308 2024

  75. [75]

    Porter-Temple R., et al., 2022, @doi [ ] 10.1093/mnras/stac1936 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.3875P 515, 3875

    work page doi:10.1093/mnras/stac1936 2022

  76. [76]

    H., Teukolsky S

    Press W. H., Teukolsky S. A., Vetterling W. T., Flannery B. P., 1992, Numerical recipes in FORTRAN. The art of scientific computing . Cambridge University Press

    1992

  77. [77]

    Robotham A. S. G., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19217.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.416.2640R 416, 2640

    work page doi:10.1111/j.1365-2966.2011.19217.x 2011

  78. [78]

    Rowlands K., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2011.19905.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.419.2545R 419, 2545

    work page doi:10.1111/j.1365-2966.2011.19905.x 2012

  79. [79]

    Saintonge A., et al., 2016, @doi [ ] 10.1093/mnras/stw1715 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.1749S 462, 1749

    work page doi:10.1093/mnras/stw1715 2016

  80. [80]

    E., et al., 2019, @doi [ ] 10.1093/mnras/sty3021 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.4617S 482, 4617

    Sansom A. E., et al., 2019, @doi [ ] 10.1093/mnras/sty3021 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.4617S 482, 4617

    work page doi:10.1093/mnras/sty3021 2019

Showing first 80 references.