Pith. sign in

REVIEW 4 major objections 5 minor 53 references

A Comparison of Full Spectral Fitting Codes for Measuring the Stellar Initial Mass Function and Other Stellar Population Properties in Elliptical Galaxies

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Which stellar initial mass function a galaxy appears to have depends on the star-formation history the fitting code assumes — and two Fornax ellipticals most likely host two stellar populations with different IMFs.

desk verdict A genuinely useful first head-to-head of IMF retrieval across four full spectral fitting codes, but the two-component astrophysical claims for NGC 1399/1404 are softer than the abstract implies. read the letter →

arxiv 2508.16525 v1 pith:NOIGDX6D submitted 2025-08-22 astro-ph.GA

Ilaria Lonoce , Anja Feldmeier-Krause , Alexandra Masegian , Wendy L. Freedman This is my paper · ORCID
classification astro-ph.GA
keywords initialmassfunctionfullspectralfittingstellarpopulationsstarformationhistoryellipticalgalaxiesFornaxclusterNGC1399NGC1404
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks how robustly full spectral fitting codes recover the stellar initial mass function (IMF) and other stellar population properties, and answers that the code's star-formation assumption largely decides the answer. Four public codes fit the same high-signal optical and near-infrared spectra of the Fornax ellipticals NGC1399 and NGC1404, using identical stellar population models. ALF and PyStaff, which assume a single simple stellar population (SSP), return one old population with a metallicity declining with radius and a flat super-Salpeter IMF, while Starlight and pPXF, which fit mixtures of populations, find two components of similar old age with different metallicities and IMF slopes. Mock-spectra tests with increasing star-formation complexity show why: SSP-assuming codes are the most precise and accurate only when the true population is a pure SSP, and become biased, with misleadingly small error bars, as soon as a second component is present. The paper concludes that the star-formation assumption is crucial for all retrieved stellar properties, and that both galaxies most likely host a double stellar population in which the majority of stars have a top-heavy IMF.

What carries the argument

Four public full spectral fitting codes, paired by design into two classes: ALF and PyStaff, which assume a single simple stellar population (SSP), and Starlight and pPXF, which solve for the best-fit weighted composition of template SSPs. All four fit the same optical+NIR spectra of NGC1399 and NGC1404 from the same Conroy et al. (2018) grid, with the low-mass IMF slope parametrized as one power-law index over 0.08–1 solar masses. The discriminating experiment is a ladder of mock spectra of increasing star-formation complexity — pure SSP; a secondary younger, metal-poor component; and two components with distinct IMF slopes — which isolates how the SFH assumption changes accuracy, precision

What would settle it

Rebuild the simulation ladder with an independent forward model: mock spectra generated from a different stellar population synthesis code and spectral library, with known two-component star-formation histories and observed-like noise, then fit with the same four codes. If ALF and PyStaff still return precise-but-biased single-population answers and Starlight/pPXF still recover both components, the central claim stands; if the ranking flips or the components blur, the result is an artifact of the shared template grid. A complementary observational check: measure the radial mass-to-light gradie

Watch

Extended reading notes

Core claim

The star-formation-history assumption made during the fit controls every retrieved stellar property, including the IMF. On the same Magellan spectra of NGC1399 and NGC1404, the SSP-assuming codes ALF and PyStaff return one old population with a flat super-Salpeter IMF, while Starlight and pPXF, which fit template mixtures, reveal two populations — a dominant metal-rich, top-heavy-IMF component and a metal-poor, bottom-heavy one whose fraction grows with radius to ~50%. Mock spectra show the SSP codes win on a pure SSP, but any second component makes them biased, with misleadingly narrow errors. The flat IMF is a trade-off between the two real components; both galaxies likely host a double st

Load-bearing premise

The simulations that anchor the interpretation assume that mock spectra drawn from the Conroy et al. (2018) template grid, with noise taken from the observed data, behave like real galaxies; because the fitting codes use that same grid, the tests measure each code's capacity to invert its own forward model, not how faithfully the grid reproduces real stellar populations.

Editorial extensions

If this is right

  • Any IMF slope, age, or metallicity measured from these galaxies under an SSP assumption — including this paper's own PyStaff-based flat super-Salpeter IMF — should be read as a mass-weighted trade-off between two real components, not as a property of a single population.
  • The widely reported radial pattern of bottom-heavy centers and Milky-Way-like outskirts may be an artifact of averaging two components whose mass fractions change with radius.
  • The local IMF–metallicity relation is not necessarily positive within a galaxy: here the metal-rich component carries the top-heavy IMF and the metal-poor component a bottom-heavy one, opposite to the SSP-based relation found in other studies.
  • Despite different masses and cluster positions, both galaxies share a similar two-component, all-old star-formation history, consistent with early accretion events during the formation of the Fornax cluster.
  • Future full spectral fitting of passive galaxies should first search for secondary components with a flexible-SFH code before applying high-precision SSP codes, ideally with a code that fits elemental abundances as free parameters within a non-parametric SFH.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: because the simulations generate mock spectra from the same Conroy et al. (2018) grid the codes fit, they measure each code's ability to invert its own forward model; if that grid misrepresents real stellar populations (abundance response functions, IMF parametrization), the code ranking and the two-component recovery could change. An independent forward model would be the dec
  • Editorial inference: the claim that the majority of stars have a top-heavy IMF depends on the light/mass fractions Starlight and pPXF assign to each component; an independent check would compare the radial mass-to-light gradient implied by the IMF slopes with dynamical mass estimates from the same velocity dispersion profiles.
  • Editorial inference: the proposed workflow — flexible-SFH code first, SSP code second — is testable on a sample of galaxies with a priori known inputs; a natural extension is applying it to galaxies currently reported to have flat IMF gradients to see whether they split into two components as these two do.
  • Editorial inference: the double-population interpretation is consistent with, but does not prove, the two-phase formation scenario; spatially resolved stellar-population maps of these galaxies (e.g., from IFU data) could confirm whether the secondary component is spatially associated with an accreted envelope.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper compares four full-spectral-fitting codes (ALF and PyStaff, which assume a single stellar population, versus Starlight and pPXF, which fit non-parametric SFHs) on the same Magellan/IMACS spectra of NGC1399 and NGC1404, and on mock spectra of increasing star-formation-history complexity. The headline claims are: (i) SSP-assuming codes are precise and accurate only for pure SSP inputs, returning biased ages, metallicities, and IMF slopes for composite populations; (ii) flexible codes detect secondary components but with larger scatter and some biases; (iii) for the two observed galaxies, ALF and PyStaff find a single old, metal-rich population with a flat super-Salpeter IMF, while Starlight and pPXF suggest two stellar components with different metallicities and IMF slopes; and (iv) the assumption on allowed SFH is crucial for all retrieved stellar properties, including the IMF. The simulations and real-data fits are mutually consistent under the paper's interpretation.

Significance. The paper addresses a timely and important question: how much of the IMF slope and other stellar-population measurements depend on the fitting code's SFH assumption. Its main strengths are the controlled comparison on the same data with the same Conroy et al. (2018) models, the use of both MCMC and Monte-Carlo error estimation, and the detailed appendices on elemental abundances, residuals, and parameter degeneracies. If the central claims hold, the paper would give useful practical guidance for future stellar-population work. However, the simulation evidence is essentially a self-recovery test within the same model grid, and the real-data two-component decomposition is made by subjective inspection of weight distributions. These limitations affect the strength of the conclusions about the two galaxies but do not negate the value of the code comparison itself.

major comments (4)
  1. [Section 3.5; Table 5] The mock spectra are generated from the Conroy et al. (2018) template grid and are then fit with the same Conroy et al. (2018) templates by all four codes. This measures each code's ability to invert its own forward model, not the fidelity of the model grid to real stellar populations. The paper uses these simulations to conclude that Starlight is reliable on real data ('we are inclined to rely only on Starlight results', Section 5) and that SSP codes 'return erroneous results' for composite SFHs. That extrapolation is load-bearing but not justified without an independent forward model (e.g., EMILES or another spectral library with independent response functions). The ranking of codes could change if the true stellar populations are not within the Conroy et al. grid. Please either add such a test or explicitly reframe the simulation claims as statements about internal model consistency.
  2. [Section 4; Figures 5-9] The detection of two stellar components in NGC1399 and NGC1404 and the component fractions in Figure 9 are obtained by 'isolating' components in the three-dimensional weights matrix 'with the help of contour plots'. This is not a formal decomposition: there is no model comparison, no criterion for when a secondary peak is significant, and no stated uncertainty on the component fractions. Given that pPXF produces spurious sub-solar metallicity peaks even for SSP mock inputs (Section 5, point 1; Figure 21), visual inspection of weight distributions is insufficient to support the claim that 'it is likely that both galaxies do have a double stellar population' (Section 5). A quantitative significance test, or a substantially weakened conclusion, is needed.
  3. [Section 5; Table 5] The CSP3 simulation was explicitly 'tuned to resemble' the real-data results for NGC1399 and NGC1404. Its successful recovery is then used as evidence that Starlight and pPXF can recover the two components in the real galaxies. This is partially circular: the simulation confirms that the codes can recover the assumed input, not that the assumed input is actually present in the observations. The paper should clearly distinguish between a simulation designed to match the proposed hypothesis and an independent validation of that hypothesis.
  4. [Section 6; Figure 9] The statement 'we have found that the majority of stars have top-heavy IMF' is not supported by the quantitative component fractions presented in Figure 9. That figure indicates that the main (top-heavy) component is dominant only in the inner half of the galaxies, while the secondary (bottom-heavy) component reaches roughly 50% near R/Re ~ 0.2. Without an aperture- or mass-weighted integration of the radial component fractions, the majority-of-stars claim is not established. The more cautious wording in Section 5 ('the super-solar metallicity component associated with a TH IMF') is consistent with the data.
minor comments (5)
  1. [Section 3.2] ALF normally permits a second younger component; the paper states this was disabled for the main comparison but enabled for some simulations. It would help to state explicitly in the simulation section which runs allowed the second component in ALF and whether this choice affects the interpretation of Figure 19.
  2. [Section 3.5] The number of realizations (10) and the noise level used for the mock spectra are mentioned only later in the appendix. Please state them in Section 3.5, since they are relevant to the precision claims in Section 5.
  3. [Section 5; footnote 3] The footnote says 'we have not tested for multiple populations', which sits oddly next to the strong language about 'a double stellar population'. Please align the wording so the exploratory nature of the two-component interpretation is consistent throughout.
  4. [Figures 12-13] The text says the middle point 'deviates by ~2σ' from the original value, but the plotted error bars and the method for computing σ are not described for this comparison figure. Please clarify what is plotted and how σ is calculated.
  5. [Section 6] The statement 'a threshold ... is unfortunately impossible to uniquely define' is a useful caveat, but the subsequent recommendation to first use Starlight-like codes and then switch to SSP codes would be more actionable if the criterion for 'confirmed to be consistent with a SSP' were specified in terms of the weight distributions shown in Figures 5 and 6.

Circularity Check

1 steps flagged · score 4.0 of 10

Partially circular validation: the CSP3 simulation used to confirm the double-component interpretation was tuned to match the very real-data results it is said to support.

  1. fitted input called prediction [Section 5 (Discussion), simulations paragraph; see also Section 3.5 and Table 5]
    "In particular, the last set of simulations was tuned to resemble the results we obtained from NGC1404 and NGC1399, i.e. two components with both ages ≳ 11 Gyr, one more metal-rich and TH and the other with lower metallicity and BH. ... By inspecting CSP3 results (Figure 19), whose mock inputs were built with stellar parameters similar to our targets, we learn that both codes should be able to retrieve their age, metallicity and IMF slope with no evident biases. In this sense we have a chance to believe that our observed data are actually revealing a double stellar component."

    The CSP3 simulation input was explicitly tuned to resemble the results already obtained from NGC1399/NGC1404. Using that simulation's successful recovery as evidence that the real data 'actually reveal a double stellar component' is circular: the recovered parameters were baked into the input by construction. The exercise shows self-consistency of the codes with a manually chosen model, not an independent confirmation. Additionally, the mock spectra were drawn from the same Conroy et al. (2018) grid used for fitting, so the test measures each code's ability to invert its own forward model. This does not invalidate the code-comparison conclusions, but it undercuts the specific claim that the double-component interpretation is independently validated.

full rationale

The paper's primary contribution is a controlled comparison of four fitting codes using the same models and data; that part is self-contained and not circular. The simulations, while generated from the same Conroy et al. (2018) grid used in the fits, are a legitimate way to test code behavior under known inputs. However, one load-bearing interpretive step is partially circular: the CSP3 simulation was tuned to match the real-data results and then used to argue that those results are believable. This is a consistency check, not independent confirmation. The paper also cites prior work by the same authors for background and for setting X1=X2, but those citations are not load-bearing for the main claim. No external benchmark or independent forward model is used, which limits the astrophysical generality of the 'double population' conclusion but does not make the code-comparison itself circular. Overall score 4 reflects one significant but localized circular validation step.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the fidelity of the Conroy et al. model grid, the subjective two-component separation, and the simulation design that reuses the same grid. No new physical entities are introduced.

free parameters (3)
  • Simulation input parameters (Table 5) = age1=13.5, age2=7.0/11.0 Gyr; [Z/H]1=0.20, [Z/H]2=-0.50/-1.00; IMF slopes 3.1/1.5, 1.1/3.5; mass fractions 80/20 or 50/5
    These inputs are chosen by hand; the CSP3 set is explicitly 'tuned to resemble' the real-data results, so successful recovery in that case does not independently validate the two-component interpretation.
  • IMF slope grid choice (X1=X2) = single slope in 0.08-1.0 Msun, range 0.5-3.5 (ALF to 3.9)
    The paper fixes the two IMF slopes equal due to observed degeneracy (Section 3, citing Lonoce et al. 2021), narrowing the IMF parametrization; this affects all IMF retrievals.
  • Elemental abundance response-function limits = e.g., PyStaff ranges mostly +/-0.45 dex, ALF -0.5 to +0.6 dex (Table 2)
    ALF and PyStaff handle non-solar abundances with different extrapolation schemes (linear vs Taylor) and different bounds, which can shift retrieved parameters; the paper acknowledges this as a source of code-dependent differences (Appendix A).
assumptions (4)
  • domain assumption Conroy et al. (2018) stellar population models and response functions faithfully represent the stellar populations of NGC1399 and NGC1404.
    All four codes fit against these templates; the paper's conclusions about IMF and two components are only as valid as this model grid (Section 3).
  • domain assumption Distinct clusters in the best-fit weight distributions correspond to physically distinct stellar populations.
    The identification of main and secondary components in Figures 5-9 assumes that weight peaks in age-metallicity-IMF space map to distinct populations, rather than to fitting degeneracies.
  • domain assumption The noise model for the simulations (noise from observed spectra) is representative of actual data noise.
    Mock spectra use noise drawn from observed spectra (Section 3.5); if noise correlations or residual systematics (telluric, fringing) are underestimated, the recovery tests may be optimistic.
  • domain assumption The IMF parametrization with fixed Salpeter slope above 1 Msun and a single low-mass slope is adequate to describe real IMF variations.
    The paper restricts to X1=X2 and does not test alternative IMF shapes (Section 3), which could affect the retrieved slopes.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Comparison of Full Spectral Fitting Codes for Measuring the Stellar Initial Mass Function and Other Stellar Population Properties in Elliptical Galaxies." pith.science (2026). https://pith.science/paper/NOIGDX6D

@misc{pith2026250816525,
  author       = {Pith},
  title        = {Pith review of: A Comparison of Full Spectral Fitting Codes for Measuring the Stellar Initial Mass Function and Other Stellar Population Properties in Elliptical Galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NOIGDX6D}},
  note         = {Machine review of arXiv:2508.16525}
}
read the original abstract

We present a comparative test of four widely used full spectral fitting codes, with the aim of answering the question: how robust is the retrieval of the stellar initial mass function (IMF) and other stellar properties of galaxies? We used ALF, PyStaff, Starlight, and pPXF to fit a set of optical+near-infrared spectroscopic data from the Magellan telescope of the two brightest galaxies in the Fornax cluster, NGC1399 and NGC1404. By fitting the same data set with the same models, we can compare the radial trends (out to ~ R_e) of IMF slope, age, metallicity and 19 elemental abundances when allowed with the four codes. To further test the robustness of our analysis, we carried out parallel simulations by creating inputs with different star formation history (SFH) complexity. The results from simulations show that codes such as ALF and PyStaff, which both assume a simple stellar population (SSP) return greater precision and accuracy only when the underlying population is a pure SSP; however, in cases where the SFH is more complex, these codes return erroneous results. Although codes like Starlight and pPXF, which retrieve the best-fit SFH without prior assumptions, tend to produce results with greater scatter and bias, they are generally more reliable in identifying secondary components. Our analysis on the two targets shows that ALF and PyStaff, that assume an SSP, give results pointing to a single old age, a decreasing metallicity with radius and a flat super-Salpeter IMF. In contrast, Starlight and pPXF suggest the presence of a secondary component with different metallicity and IMF characteristics.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

53 extracted references · 8 canonical work pages

  1. [1]

    D., Smith, R

    Alton, P. D., Smith, R. J., & Lucey, J. R. 2017, MNRAS, 468, 1594, doi: 10.1093/mnras/stx464

    doi:10.1093/mnras/stx464 2017

  2. [2]

    E., Spiniello, C., Arnaboldi, M., et al

    Barbosa, C. E., Spiniello, C., Arnaboldi, M., et al. 2021, A&A, 649, A93, doi: 10.1051/0004-6361/202039809 38 Lonoce et al. ����� �� Residuals average values in the optical region. ALF PyStaff Starlight pPXF NGC1399 CENTER 0.6 % 0.6 % 4.1 % 2.1 % NGC1399 CENTER* - - 3.0 % 1.1 % NGC1399 OUTER 0.8 % 0.8 % 3.7 % 1.4 % NGC1399 OUTER* - - 3.4 % 1.1 % NGC1404 C...

    doi:10.1051/0004-6361/202039809 2021

  3. [3]

    Bensby, T., Feltzing, S., & Oey, M. S. 2014, A&A, 562, A71, doi: 10.1051/0004-6361/201322631

    doi:10.1051/0004-6361/201322631 2014

  4. [4]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692

    doi:10.1086/308692 2000

  5. [5]

    2017, MNRAS, 466, 798, doi: 10.1093/mnras/stw3020

    Cappellari, M. 2017, MNRAS, 466, 798, doi: 10.1093/mnras/stw3020

    doi:10.1093/mnras/stw3020 2017

  6. [6]

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

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

    doi:10.1086/381875 2004

  7. [7]

    M., Alatalo, K., et al

    Cappellari, M., McDermid, R. M., Alatalo, K., et al. 2012, Nature, 484, 485, doi: 10.1038/nature10972

    doi:10.1038/nature10972 2012

  8. [8]

    2014, ApJ, 796, 75, doi: 10.1088/0004-637X/796/2/75

    Chabrier, G., Hennebelle, P., & Charlot, S. 2014, ApJ, 796, 75, doi: 10.1088/0004-637X/796/2/75

    doi:10.1088/0004-637x/796/2/75 2014

Show all 53 references
  1. [9]

    2016, MNRAS, 462, 1415, doi: 10.1093/mnras/stw1756 Cid Fernandes, R

    Chevallard, J., & Charlot, S. 2016, MNRAS, 462, 1415, doi: 10.1093/mnras/stw1756 Cid Fernandes, R. 2018, MNRAS, 480, 4480, doi: 10.1093/mnras/sty2012 Cid Fernandes, R., Mateus, A., Sodr´ e, L., Stasi´ nska, G., &

    2016 doi

  2. [11]

    2012, ApJ, 747, 69, doi: 10.1088/0004-637X/747/1/69

    Conroy, C., & van Dokkum, P. 2012, ApJ, 747, 69, doi: 10.1088/0004-637X/747/1/69

    2012 doi

  3. [12]

    G., & Lind, K

    Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, ApJ, 854, 139, doi: 10.3847/1538-4357/aaab49

    2018 doi

  4. [13]

    C., & Osip, D

    Dressler, A., Hare, T., Bigelow, B. C., & Osip, D. J. 2006, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 6269, IMACS: the wide-field imaging spectrograph on Magellan-Baade, 62690F, doi: 10.1117/12.670573

    2006 doi

  5. [14]

    N., O’Connell, R

    Fanelli, M. N., O’Connell, R. W., Burstein, D., & Wu, C.-C. 1990, ApJ, 364, 272, doi: 10.1086/169411

    1990 doi

  6. [15]

    Feldmeier-Krause, A., Lonoce, I., & Freedman, W. L. 2020, ApJ, 902, 12, doi: 10.3847/1538-4357/abafb3 —. 2021, ApJ, 923, 65, doi: 10.3847/1538-4357/ac281e

    2020 doi

  7. [16]

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

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  8. [17]

    2019, MNRAS, 485, 1675, doi: 10.1093/mnras/stz418

    Ge, J., Mao, S., Lu, Y., Cappellari, M., & Yan, R. 2019, MNRAS, 485, 1675, doi: 10.1093/mnras/stz418

    2019 doi

  9. [18]

    2018, MNRAS, 478, 2633, doi: 10.1093/mnras/sty1245

    Ge, J., Yan, R., Cappellari, M., et al. 2018, MNRAS, 478, 2633, doi: 10.1093/mnras/sty1245

    2018 doi

  10. [19]

    M., & Papaderos, P

    Gomes, J. M., & Papaderos, P. 2017, A&A, 603, A63, doi: 10.1051/0004-6361/201628986 Gon¸ calves, G., Coelho, P., Schiavon, R., & Usher, C. 2020, MNRAS, 499, 2327, doi: 10.1093/mnras/staa3051

    2017 doi

  11. [20]

    C., Li, Z.-Y., Barth, A

    Ho, L. C., Li, Z.-Y., Barth, A. J., Seigar, M. S., & Peng, C. Y. 2011, ApJS, 197, 21, doi: 10.1088/0067-0049/197/2/21 Comparison of FSF 39 ������ ��� Simulation results - Elemental abundances - SSP. Comparison of ALF results (red) and PyStaff results (blue) in retrieving the e...

    2011 doi

  12. [21]

    Hopkins, A. M. 2018, PASA, 35, e039, doi: 10.1017/pasa.2018.29

    2018 doi

  13. [22]

    Hopkins, P. F. 2013, MNRAS, 433, 170, doi: 10.1093/mnras/stt713

    2013 doi

  14. [23]

    2016, ApJ, 820, 42, doi: 10.3847/0004-637X/820/1/42

    Iodice, E., Capaccioli, M., Grado, A., et al. 2016, ApJ, 820, 42, doi: 10.3847/0004-637X/820/1/42

    2016 doi

  15. [24]

    2019, A&A, 627, A136, doi: 10.1051/0004-6361/201935721

    Iodice, E., Sarzi, M., Bittner, A., et al. 2019, A&A, 627, A136, doi: 10.1051/0004-6361/201935721

    2019 doi

  16. [25]

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

    Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22, doi: 10.3847/1538-4365/abef67

    2021 doi

  17. [26]

    2015, A&A, 576, A78, doi: 10.1051/0004-6361/201423909

    Kausch, W., Noll, S., Smette, A., et al. 2015, A&A, 576, A78, doi: 10.1051/0004-6361/201423909

    2015 doi

  18. [27]

    2009, A&A, 501, 1269, doi: 10.1051/0004-6361/200811467

    Koleva, M., Prugniel, P., Bouchard, A., & Wu, Y. 2009, A&A, 501, 1269, doi: 10.1051/0004-6361/200811467

    2009 doi

  19. [28]

    2008, MNRAS, 385, 1998, doi: 10.1111/j.1365-2966.2008.12908.x

    Soubiran, C. 2008, MNRAS, 385, 1998, doi: 10.1111/j.1365-2966.2008.12908.x

    2008

  20. [29]

    2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x La Barbera, F., Ferreras, I., Vazdekis, A., et al

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x La Barbera, F., Ferreras, I., Vazdekis, A., et al. 2013, MNRAS, 433, 3017, doi: 10.1093/mnras/stt943 La Barbera, F., Vazdekis, A., Ferreras, I., et al. 2016, MNRAS, 457, 1468, doi: 10.1093/mnras/stv2996 —....

    2001

  21. [30]

    Lonoce, I., Feldmeier-Krause, A., & Freedman, W. L. 2021, ApJ, 920, 93, doi: 10.3847/1538-4357/ac11f9

    2021 doi

  22. [31]

    L., & Feldmeier-Krause, A

    Lonoce, I., Freedman, W. L., & Feldmeier-Krause, A. 2023, ApJ, 948, 65, doi: 10.3847/1538-4357/acc025 Mart ´ ın-Navarro, I., La Barbera, F., Vazdekis, A., Falc´ on-Barroso, J., & Ferreras, I. 2015a, MNRAS, 447, 1033, doi: 10.1093/mnras/stu2480 Mart ´ ın-Navarro, I., Vazdekis, ...

    2023 doi

  23. [32]

    E., Sivanandam, S., & Moon, D.-S

    Meyer, R. E., Sivanandam, S., & Moon, D.-S. 2019, ApJ, 875, 151, doi: 10.3847/1538-4357/ab11d2

    2019 doi

  24. [33]

    H., & Ostriker, J

    Naab, T., Johansson, P. H., & Ostriker, J. P. 2009, ApJL, 699, L178, doi: 10.1088/0004-637X/699/2/L178

    2009 doi

  25. [34]

    2010, ApJ, 725, 2312, doi: 10.1088/0004-637X/725/2/2312

    Burkert, A. 2010, ApJ, 725, 2312, doi: 10.1088/0004-637X/725/2/2312

    2010 doi

  26. [35]

    Osterbrock, D. E. 1989, Astrophysics of gaseous nebulae and active galactic nuclei

    1989

  27. [36]

    2018, MNRAS, 477, 3954, doi: 10.1093/mnras/sty785

    Parikh, T., Thomas, D., Maraston, C., et al. 2018, MNRAS, 477, 3954, doi: 10.1093/mnras/sty785

    2018 doi

  28. [37]

    S., Ward, M

    Reynolds, C. S., Ward, M. J., Fabian, A. C., & Celotti, A. 1997, MNRAS, 291, 403, doi: 10.1093/mnras/291.3.403

    1997 doi

  29. [38]

    Salpeter, E. E. 1955, ApJ, 121, 161, doi: 10.1086/145971

    1955 doi

  30. [39]

    L., De Propris, R., et al

    Saracco, P., Barbera, F. L., De Propris, R., et al. 2023, MNRAS, 520, 3027, doi: 10.1093/mnras/stad241

    2023 doi

  31. [40]

    2018, MNRAS, 478, 4084, doi: 10.1093/mnras/sty1092

    Sarzi, M., Spiniello, C., La Barbera, F., Krajnovi´ c, D., & van den Bosch, R. 2018, MNRAS, 478, 4084, doi: 10.1093/mnras/sty1092

    2018 doi

  32. [41]

    L., et al

    Sarzi, M., Falc´ on-Barroso, J., Davies, R. L., et al. 2006, MNRAS, 366, 1151, doi: 10.1111/j.1365-2966.2005.09839.x

    2006

  33. [42]

    Schiavon, R. P. 2007, ApJS, 171, 146, doi: 10.1086/511753

    2007 doi

  34. [43]

    2015, A&A, 576, A77, doi: 10.1051/0004-6361/201423932

    Smette, A., Sana, H., Noll, S., et al. 2015, A&A, 576, A77, doi: 10.1051/0004-6361/201423932

    2015 doi

  35. [44]

    R., et al

    Spavone, M., Capaccioli, M., Napolitano, N. R., et al. 2017, A&A, 603, A38, doi: 10.1051/0004-6361/201629111

    2017 doi

  36. [45]

    2005, ApJ, 621, 673, doi: 10.1086/426932

    Oliveira, C. 2005, ApJ, 621, 673, doi: 10.1086/426932

    2005 doi

  37. [46]

    1993, in Astronomical Society of the Pacific Conference Series, Vol

    Tody, D. 1993, in Astronomical Society of the Pacific Conference Series, Vol. 52, Astronomical Data Analysis Software and Systems II, ed. R. J. Hanisch, R. J. V. Brissenden, & J. Barnes, 173

    1993

  38. [47]

    C., Worthey, G., Faber, S

    Trager, S. C., Worthey, G., Faber, S. M., Burstein, D., & Gonz´ alez, J. J. 1998, ApJS, 116, 1, doi: 10.1086/313099 van Dokkum, P., Conroy, C., Villaume, A., Brodie, J., &

    1998 doi

  39. [48]

    Romanowsky, A. J. 2017, ApJ, 841, 68, doi: 10.3847/1538-4357/aa7135 van Dokkum, P. G., & Conroy, C. 2011, ApJL, 735, L13, doi: 10.1088/2041-8205/735/1/L13

    2017 doi

  40. [49]

    2011, MNRAS, 412, 2017, doi: 10.1111/j.1365-2966.2010.18036.x

    Schmidtobreick, L. 2011, MNRAS, 412, 2017, doi: 10.1111/j.1365-2966.2010.18036.x

    2011

  41. [50]

    Houghton, R. C. W. 2018a, MNRAS, 479, 2443, doi: 10.1093/mnras/sty1434 —. 2018b, MNRAS, 475, 1073, doi: 10.1093/mnras/stx3199

    doi

  42. [51]

    2016, MNRAS, 463, 3409, doi: 10.1093/mnras/stw2231

    Vazdekis, A., Koleva, M., Ricciardelli, E., R¨ ock, B., & Falc´ on-Barroso, J. 2016, MNRAS, 463, 3409, doi: 10.1093/mnras/stw2231

    2016 doi

  43. [52]

    2013, MNRAS, 435, 2274, doi: 10.1093/mnras/stt1445

    Weidner, C., Ferreras, I., Vazdekis, A., & La Barbera, F. 2013, MNRAS, 435, 2274, doi: 10.1093/mnras/stt1445

    2013 doi

  44. [53]

    M., Maraston, C., Goddard, D., Thomas, D., & Parikh, T

    Wilkinson, D. M., Maraston, C., Goddard, D., Thomas, D., & Parikh, T. 2017, MNRAS, 472, 4297, doi: 10.1093/mnras/stx2215

    2017 doi

  45. [54]

    M., Gonzalez, J

    Worthey, G., Faber, S. M., Gonzalez, J. J., & Burstein, D. 1994, ApJS, 94, 687, doi: 10.1086/192087 Comparison of FSF 41 ������ ��� Simulation results - Elemental abundances - CSP. Same description as in Figure 24. 42 Lonoce et al. ������ ��� Simulation results - Elemental abu...

    1994 doi

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.