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Insight into the physical processes that shape the metallicity profiles in galaxies

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Broken shapes of galaxy metallicity profiles can be read as a record of gas inflow, star formation, and feedback-driven outflows, not as fitting artifacts.

desk verdict Useful new fitting algorithm and honest case-study physics, but the abstract sells a two-galaxy result as a general diagnostic. read the letter →

arxiv 2502.02080 v2 pith:JMQUSRU3 submitted 2025-02-04 astro-ph.GA

Brian Tapia-Contreras , Patricia B. Tissera , Emanuel Sillero , Jenny Gonzalez-Jara , Catalina Casanueva-Villarreal , Susana Pedrosa , Lucas Bignone , Nelson D. Padilla
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Rosa Domínguez-Tenreiro
This is my paper · ORCID
classification astro-ph.GA
keywords galaxies:abundancesevolutionformationISMmetallicitygradientsgalacticfeedbackcosmologicalsimulationsoxygenabundanceprofiles
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

The paper aims to show that the radial profile of oxygen abundance in a galaxy's star-forming gas is a structured record of how gas enters, enriches, and leaves the galaxy, not just a smooth gradient. Analysing 45 simulated central galaxies with masses between $10^{8.5}$ and $10^{10.5}$ solar masses at $z=0$, the authors find that only about 38 per cent of profiles are single power laws; the rest contain inner breaks, outer breaks, or both, and the gradient values match observations. Tracking two Local Group analogue galaxies back to $z\sim6$, they tie inner rises to gas inflow followed by star formation, inner drops to feedback-driven outflows of enriched gas, outer rises to re-accretion of enriched circumgalactic gas and extended star formation, and outer drops to metal-poor cold-flow accretion at high redshift. The payoff is that the shape of a metallicity profile can be used as a diagnostic of the gas cycle inside galaxies.

What carries the argument

The load-bearing tool is the DB-A algorithm, a piecewise-linear fitter that removes outliers with a Gaussian kernel density estimate, allows up to two breaks in the oxygen profile, and selects the number of breaks by comparing relative fit errors against a tolerance parameter ($\tau=0.99$); it defines the inner, middle, and outer slopes that the analysis maps onto physical processes. The second mechanism is the gas bookkeeping applied to the two case-study galaxies: gas particles entering each radial region are classified by origin (unbound cold flows, circumgalactic gas, satellite gas, inflowing or outflowing interstellar gas) and by radial and vertical displacement, with star formation and accretion rates computed per region. This combination turns a one-dimensional abundance profile into an accounting of where the gas comes from, where it travels, and what it does when it arrives.

What would settle it

Re-fit the 45 simulated oxygen profiles with an independent, fully automated piecewise-linear fitter using a fixed model-selection rule and no manual case-by-case adjustments; if the resulting fractions of linear, inner-broken, outer-broken, and doubly-broken profiles differ materially from 38/13/40/9 per cent, the break taxonomy and the physical interpretations attached to it are not robust.

Watch

Extended reading notes

Core claim

The central discovery is a break taxonomy with a physical cause attached to each entry. Using the DB-A algorithm, which fits piecewise power laws with up to two breaks to the running-median oxygen profile, the paper classifies the $z=0$ sample into 38 per cent linear, 40 per cent outer-broken, 13 per cent inner-broken, and 9 per cent doubly-broken profiles, with middle-region slopes that agree with single-gradient measurements in MUSE and CALIFA galaxies. In the two case studies traced across time, inner breaks occur in 26 per cent of snapshots (72 per cent inner rises, 28 per cent inner drops) and outer breaks in 43 per cent (68 per cent outer rises, 32 per cent outer drops). The authors attribute inner rises to cold gas falling into the centre followed by a starburst that raises central oxygen, inner drops to supernova-driven ejection of enriched gas, outer rises to the return of enriched material from the circumgalactic reservoir plus extended star formation and tidally driven mixing, and outer drops, found almost exclusively at $z>1$, to accretion of pristine cold-flow gas. The claim is that each break shape advertises a specific episode in the cycle of accretion, star formation, and outflow.

Load-bearing premise

The classification of every profile as linear, inner-broken, outer-broken, or doubly-broken rests on the DB-A algorithm's decision about the true number and position of breaks, with ambiguous 'conflictive' cases settled by manual revision rather than a fixed objective rule; if those decisions are biased, the reported break fractions and the process attributions built on them shift.

Editorial extensions

If this is right

  • Middle-region gradients from DB-A agree with single linear fits over $[0.5,1.5]\,r_{50}$, so past observational comparisons based on one gradient remain meaningful even when the true profile is broken.
  • Inner breaks last about 1.5 Gyr in the massive case study versus about 0.3 Gyr in the lower-mass one, so break persistence carries information about how deep the galaxy's potential well is.
  • Inner drops and inverted central gradients are produced by the same feedback-driven ejection of enriched gas, so they should appear together and be accompanied by disturbed gas morphology.
  • Outer drops are a high-redshift feature in these simulations, tied to metal-poor cold flows; mapping them at $z>1$ with deep spectroscopy would test whether the simulated cold-flow channel operates in real galaxies.
  • Breaks migrate outward in units of $r_{50}$ as enrichment proceeds, so the location of a break can serve as a rough clock for recent accretion and star-formation episodes.

Reading between the lines

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

  • The same break taxonomy could be applied to stellar-population abundance maps rather than gas; if the gas-cycle interpretation is right, starlight-weighted profiles should show smoothed echoes of the same breaks, shifted by the ages of the stars.
  • Because these simulations lack AGN feedback and stop at $10^{10.5}$ solar masses, the near absence of inner drops may be a mass-range effect; running DB-A on simulations with AGN feedback or on more massive galaxies would test whether inner drops become as common as observed.
  • The tolerance parameter and the two-break maximum are modeling choices; varying them systematically would reveal whether the 38/13/40/9 per cent split is stable or an artifact of the selection rule, and would calibrate the method for noisier observational data.
  • Observational outer breaks are likely under-counted because of surface-brightness limits; if profile shape is a true gas-cycle diagnostic, deeper IFU observations at fixed mass should find a higher outer-break fraction than current surveys report.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper uses the CIELO cosmological zoom simulations to characterize the gas-phase oxygen abundance profiles of 45 central galaxies at z=0 and to interpret profile breaks physically. The authors introduce a new fitting algorithm, DB-A, which fits up to two broken power laws and classifies profiles as linear, inner-broken, outer-broken, or doubly broken. At z=0 they report 38 per cent linear, 13 per cent inner, 40 per cent outer, and 9 per cent double breaks, with gradient values consistent with MUSE/CALIFA/MaNGA observations. They then follow two galaxies in a Local Group analog across about 87 snapshots to z~6, finding that inner rises are associated with gas inflows and subsequent star formation, inner drops with feedback-driven outflows, outer rises with re-accretion and CGM mixing, and outer drops with metal-poor cold-flow accretion. The paper concludes that metallicity-profile shapes can serve as diagnostics of these processes.

Significance. If the claims hold, the paper provides a simulation-based interpretation of broken metallicity profiles that ties break types to specific baryon-cycle processes, and it offers a new automatic fitting tool that is benchmarked against Theil-Sen single-slope fits and against observational break statistics. The authors are candid about the main limitations: no AGN feedback, restriction to sub-Milky-Way masses, and the case-study nature of the process attributions. The strength of the paper is its use of full cosmological simulations with metal-dependent cooling, star formation, and SN feedback, and its detailed temporal tracking of two galaxies, including a quantitative decomposition of accretion sources. The main risks are the subjectivity in the break-detection step and the extrapolation from two case studies to population-level statements.

major comments (4)
  1. [2.2] The description of the DB-A algorithm leaves a load-bearing subjective step unspecified. The text states that "conflictive cases (i.e. very small or very large break radius or inner and outer break radius too close to each other) were manually revised, decreasing the number of breaks used to fit the profiles," but no quantitative thresholds are given for "very small," "very large," or "too close." Because every downstream result — the z=0 profile fractions (Section 3), the temporal break statistics (Section 4: 26 per cent inner, 43 per cent outer, 72 per cent inner rises, 68 per cent outer rises), and the case-study attributions (Section 5) — depends on the number and location of breaks, this manual intervention could bias all of them. The authors should specify the criteria in advance and should show that the reported fractions are stable when the revision step is replaced by an objective rule (for example, requiring the break radius to lie within a fixed range and requiring separated inner and outer breaks).
  2. [2.2 and Table A.1] The fitted gradients and break radii are presented without any uncertainty estimates. Table A.1 lists single values for each galaxy, and the time series in Figure 8 are plotted without error bars, although the underlying profiles are noisy, as Figure 1 shows broad abundance distributions at fixed radius. Without uncertainties one cannot assess whether the 38/40/13/9 per cent distribution is distinguishable from, say, a 30/40/15/15 distribution, nor whether the 72 per cent inner-rise fraction or the redshift dependence of outer drops is statistically significant. The authors should provide bootstrap or resampling errors on the gradients and break radii and propagate them to the quoted fractions.
  3. [2.2 and Section 3] The robustness of DB-A to its hand-set parameters is not demonstrated. The running-median window N=40, the KDE outlier threshold 0.05, the tolerance tau=0.99, and nmax=2 are chosen once and used everywhere; the only validation reported is a statement that BIC and AIC give similar results. Since the break-detection method is the paper's central tool, the authors should show how the z=0 fractions and the temporal classifications change under plausible variations of N, tau, and the outlier threshold (for example, N=20 and N=80), and they should describe the BIC/AIC comparison in enough detail to be checked.
  4. [Abstract and Sections 4-5] The abstract and conclusions generalize the physical attributions to the population: "Most inner breaks show central oxygen enhancement, linked to gas accretion and star formation. Inner drops result from disrupted gas due to feedback-driven outflows. Outer breaks with high metallicities arise from re-accreted material..." However, the quantitative link between break type and measured inflow/outflow/accretion properties is established only for the two case-study galaxies LG1-4469 and LG1-4337 (Sections 4-5). The z=0 sample (Section 3) provides frequencies of profile shapes but not measurements of the physical processes responsible for those shapes. The authors themselves note in Section 5 that these results "will be used to define a strategy to perform a statistical study in a forthcoming paper." As it stands, the paper should either soften the population-level wording or add a quantitative test, such as comparing the dominant accretion source, outflow strength, or SFR enhancement between break types across the full sample.
minor comments (6)
  1. [2.2] The abbreviation B17 is used in the phrase "B17 and SM18" but is never defined; it presumably refers to Belfiore et al. 2017, which should be cited explicitly at that point.
  2. [Throughout] There are several typos that should be corrected in a revision: "Form the upper panels" (Sec. 5.4), "ouflows" (Sec. 4.2), "statical study" (Sec. 5), "high redshit" (Sec. 5.1), "shirring sphere" (Sec. 2.1), "understating" (Conclusions), and a duplicated "showing showing" in the caption of Fig. 12.
  3. [Table A.1] The table header labels the columns as "Inner, Middle, Outer" but the caption does not explicitly say that the middle value is the DB-A middle-region gradient and that linear profiles use the full [epsilon, 2 r83] range; please state this in the caption.
  4. [Figure 3] The observational comparison would be more informative if the SM18 break categories (linear, inner, outer, double) were shown in the same panels; currently only their linear-gradient values are overplotted in the lower panels.
  5. [Section 3] The binomial probability quoted for the difference in break fractions between CIELO and SM18 should be accompanied by the mass-range caveat already mentioned in the text, and a chi-square or Anderson-Darling test on the category counts would be more natural than a binomial test on mutually exclusive fractions of one sample.
  6. [4.1.3 and Figs. 9/12] The sign convention delta_r < 0 for outward flow is opposite to the usual radial-coordinate convention and is easy to misread; please state it explicitly in the captions of Figs. 9 and 12 as well as in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the physical-process attributions are based on independently measured gas accretion, star formation, and merger diagnostics, not on the fitted profile parameters themselves.

full rationale

The paper's central claims are not derived from assumptions that already contain the conclusions. Break detection with DB-A fits piecewise linear slopes to simulated oxygen abundance profiles; this is a descriptive measurement step, not a prediction built from the physical diagnostics. The physical attributions (inner rises from gas inflows plus star formation; inner drops from feedback-driven outflows; outer rises from re-accreted CGM gas and mixing; outer drops from metal-poor cold-flow accretion) are supported by independent per-particle diagnostics: accretion source fractions (satellite gas, unbound gas, CGM gas, inflowing/outflowing ISM) measured by tracing gas particles between snapshots, SFR and SFR surface density in the same regions, particle displacements, temperatures, enrichment relative to the region, and merger/interaction histories. These diagnostics are not fitted to the profile-shape classifications; the classifications use only the radial oxygen profile, while the process variables use kinematics, temperature, progenitor origin, and independent simulation quantities. The z=0 comparison of simulated gradients and break radii against MUSE, CALIFA, TYPHOON, and MaNGA observations provides an external benchmark that is not constrained by the paper's fitted values. The only notable self-citations (CIELO simulation description in Tissera et al. 2025; earlier process studies such as Sillero et al. 2017) are normal references to prior work and are not used as a uniqueness theorem or as a substitute for the new temporal analysis. The paper explicitly frames the two-galaxy analysis as illustrative and announces a future statistical study, so any concern about generalization is a scope or completeness issue, not circularity. No equation or fitted parameter is reused as both input and output; no prediction reduces to its own construction.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the fidelity of the CIELO simulations, the representativeness of the 45-galaxy sample, and the objectivity of the DB-A break-detection algorithm. The paper introduces no new physical entities; its free parameters are analysis choices (tau, window size, outlier threshold, break classification) that directly affect the reported break statistics.

free parameters (5)
  • DB-A tolerance tau = 0.99
    Chosen in Section 2.2 to decide whether a more complex piecewise fit is preferred over a simpler one; the break statistics depend critically on this threshold.
  • Running median window N = 40 particles
    Used to construct smooth radial abundance profiles in Section 2.2; a different N could alter break detection.
  • KDE outlier threshold = 0.05
    Particles with KDE density below this are removed before fitting in Section 2.2; affects the fitted slopes and breaks.
  • Break classification threshold = 1.5 r50
    In Section 2.2 a single break is classified as inner if r_br<1.5 r50 and outer otherwise, based on B17 and SM18; this determines the physical interpretation of each break.
  • Maximum number of breaks nmax = 2
    Set to two based on observational constraints in Section 2.2, limiting the complexity of profiles.
assumptions (5)
  • domain assumption The CIELO subgrid model (density threshold 1e-26 g cm^-3, T<15000 K, convergent flow for star formation; SNII yields from Woosley & Weaver 1995; SNIa from Mosconi et al. 2001) faithfully reproduces the chemical enrichment and feedback relevant to metallicity gradients.
    All results come from these simulations; if the subgrid physics is not realistic (e.g., no AGN feedback, no AGB enrichment as noted in Section 2), the inferred physical origins of breaks could be wrong.
  • domain assumption The 45 CIELO central galaxies with M* between 10^8.5 and 10^10.5 Msun at z=0 are a representative sample of the local galaxy population in that mass range.
    The z=0 statistics (fractions of linear, inner, outer, double breaks) assume this sample represents the broader population, despite selection of initial conditions around specific halos.
  • ad hoc to paper The DB-A algorithm with tau=0.99 and manual revision identifies the true number and location of breaks without systematic bias.
    The tolerance and manual revision criteria in Section 2.2 are not justified outside the paper; biased break detection would invalidate the statistical claims.
  • ad hoc to paper The running median over 40 radially ordered particles preserves the underlying radial metallicity profile shape.
    If smoothing is too aggressive, real breaks could be erased; if too weak, noise could be fit as breaks.
  • domain assumption Oxygen abundance in cool dense gas traces the physical processes (inflows, outflows, accretion) responsible for the breaks.
    The analysis uses 12+log(O/H) in star-forming regions; if oxygen does not trace metal enrichment or is affected by the chosen yields, the interpretation changes.

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Cite this review

Pith. "Pith review of Insight into the physical processes that shape the metallicity profiles in galaxies." pith.science (2026). https://pith.science/paper/JMQUSRU3

@misc{pith2026250202080,
  author       = {Pith},
  title        = {Pith review of: Insight into the physical processes that shape the metallicity profiles in galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMQUSRU3}},
  note         = {Machine review of arXiv:2502.02080}
}
abstract

The distribution of chemical elements in the star-forming regions can store information on the chemical enrichment history of the galaxies. Negative metallicity gradients are expected in galaxies forming inside-out. However, observations show that the metallicity profiles can be broken. We aim to study the diversity of metallicity profiles that can arise in the current cosmological context and compare them with available observations. We also seek to identify the physical processes responsible for breaks in metallicity profiles by using two galaxies as case studies. We analyze central galaxies from the cosmological simulations of the CIELO project, within the stellar mass range [$10^{8.5}$, $10^{10.5}$] M$_\odot$ at $z=0$. A new algorithm, DB-A, was developed to fit multiple power laws to the metallicity profiles, enabling a flexible assessment of metallicity gradients in various galactic regions. The simulations include detailed modeling of gas, metal-dependent cooling, star formation, and supernova feedback. At $z=0$, we find diverse profile shapes, including inner and outer drops and rises, with some galaxies exhibiting double breaks. Gradient values align with observations. A temporal analysis of Local Group analogs shows inner and outer breaks occurring at all cosmic times, with outer breaks being more frequent. Metallicity gradients show high variability at high redshift, transitioning to mild evolution at lower redshift. Most inner breaks show central oxygen enhancement, linked to gas accretion and star formation. Inner drops result from disrupted gas due to feedback-driven outflows. Outer breaks with high metallicities arise from re-accreted material, extended star formation, and CGM-driven gas mixing. Outer drops are common at high redshift, linked to metal-poor gas accretion from cold flows. We highlight the complex interplay of these processes which often act together.

Figures

Figures reproduced from arXiv: 2502.02080 by the authors.

Figure 1
Figure 1. Examples of the variety of metallicity profiles in the CIELO galaxies. From left to right: (i) Face-on and edge-on (top and [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Comparison between the metallicity gradients (filled [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Stellar mass-, sSFR-, size-metallicity gradient relation for the 45 star-forming central galaxies (left, middle and right panels, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Similar to Fig [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Inner (teal symbols) and outer (orange symbols) break radii as a function of: (i) galaxy stellar mass (left panel); (ii) sSFR [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Time evolution of the SFR (shaded areas) and the total accretion rates ( [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Fractions of the total accreted gas (M˙ ; see [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Evolution of the metallicity gradients as a function of [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: δr − δz diagram illustrating the motion of the gas accreted into the inner region of LG1-4337 between lookback time ∼ 4.8 Gyr and ∼ 3 Gyr. During this period, the galaxy exhibits an inner break of its metallicity profile. Left panel: KDE distribution of the accreted pa…
Figure 10
Figure 10. Figure 10: Temporal sequence showing the evolution of inner rise metallicity profiles for LG1-4469 (first and second rows) and LG1- [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Examples of morphologically disrupted galaxies showing evidence of central gas ejection driven by strong energetic feed [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: δr − δz diagrams showing showing the motion of the gas accreted into the outer region of LG1-4337 from different sources (similar to [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Similar to Fig [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Similar to Fig [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]

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Works this paper leans on

100 extracted references · 46 canonical work pages

  1. [1]

    2024, A&A, 688, A146 Astropy Collaboration, Price-Whelan, A

    Arribas, S., Perna, M., Rodríguez Del Pino, B., et al. 2024, A&A, 688, A146 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

    2024

  2. [2]

    Barnes, J. E. & Hernquist, L. 1996, ApJ, 471, 115

    1996

  3. [3]

    2017, MNRAS, 469, 151

    Belfiore, F., Maiolino, R., Tremonti, C., et al. 2017, MNRAS, 469, 151

    2017

  4. [4]

    2015, py-sphviewer: Py-SPHViewer v1.0.0

    Benitez-Llambay, A. 2015, py-sphviewer: Py-SPHViewer v1.0.0

    2015

  5. [5]

    2008, AJ, 136, 2846

    Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846

    2008

  6. [6]

    C., & Ryan-Weber, E

    Bresolin, F., Kennicutt, R. C., & Ryan-Weber, E. 2012, ApJ, 750, 122

    2012

  7. [7]

    C., & Goddard, Q

    Bresolin, F., Ryan-Weber, E., Kennicutt, R. C., & Goddard, Q. 2009, ApJ, 695, 580

    2009

  8. [8]

    A., Law, D

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7

    2015

Show all 100 references
  1. [9]

    L., Patton, D

    Bustamante, S., Ellison, S. L., Patton, D. R., & Sparre, M. 2020, MNRAS, 494, 3469

    2020

  2. [10]

    Cardoso, A. F. S., Cavichia, O., Mollá, M., & Sánchez-Menguiano, L. 2025, ApJ, 980, 45

    2025

  3. [11]

    2018, MNRAS, 478, 4293

    Carton, D., Brinchmann, J., Contini, T., et al. 2018, MNRAS, 478, 4293

    2018

  4. [12]

    B., Padilla, N., et al

    Casanueva-Villarreal, C., Tissera, P. B., Padilla, N., et al. 2024, A&A, 688, A183

    2024

  5. [13]

    E., Tissera, P

    Cataldi, P., Pedrosa, S. E., Tissera, P. B., et al. 2023, MNRAS, 523, 1919

    2023

  6. [14]

    2016, MNRAS, 457, 2605

    Ceverino, D., Sánchez Almeida, J., Muñoz Tuñón, C., et al. 2016, MNRAS, 457, 2605

    2016

  7. [15]

    2003, ApJ, 586, L133

    Chabrier, G. 2003, ApJ, 586, L133

    2003

  8. [16]

    J., et al

    Chen, Q.-H., Grasha, K., Battisti, A. J., et al. 2023, MNRAS, 519, 4801

    2023

  9. [17]

    B., et al

    Cornejo, A., Sillero, E., Tissera, P. B., et al. 2025, Accepted for publication in A&A, Article reference: aa52226-24 Davé, R., Finlator, K., & Oppenheimer, B. D. 2011, MNRAS, 416, 1354

    2025

  10. [18]

    S., & White, S

    Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, ApJ, 292, 371

    1985

  11. [19]

    & Silk, J

    Dekel, A. & Silk, J. 1986, ApJ, 303, 39

    1986

  12. [20]

    Diaz, A. I. 1989, in Evolutionary Phenomena in Galaxies, ed. J. E. Beckman & B. E. J. Pagel, 377

    1989

  13. [21]

    2009, MNRAS, 399, 497

    Dolag, K., Borgani, S., Murante, G., & Springel, V . 2009, MNRAS, 399, 497

    2009

  14. [22]

    Easeman, B., Schady, P., Wuyts, S., & Yates, R. M. 2022, MNRAS, 511, 371

    2022

  15. [23]

    L., Mendel, J

    Ellison, S. L., Mendel, J. T., Patton, D. R., & Scudder, J. M. 2013, MNRAS, 435, 3627

    2013

  16. [24]

    L., Thorp, M

    Ellison, S. L., Thorp, M. D., Pan, H.-A., et al. 2020, MNRAS, 492, 6027

    2020

  17. [25]

    Fall, S. M. & Efstathiou, G. 1980, MNRAS, 193, 189

    1980

  18. [26]

    2016, MNRAS, 462, L41

    Fragkoudi, F., Athanassoula, E., & Bosma, A. 2016, MNRAS, 462, L41

    2016

  19. [27]

    M., et al

    Franchetto, A., Mingozzi, M., Poggianti, B. M., et al. 2021, ApJ, 923, 28 Gámez-Marín, M., Santos-Santos, I., Domínguez-Tenreiro, R., et al. 2024, ApJ, 965, 154

    2021

  20. [28]

    K., Carigi, L., et al

    Garay-Solis, Y ., Barrera-Ballesteros, J. K., Carigi, L., et al. 2024, MNRAS, 533, 880

    2024

  21. [29]

    M., Torrey, P., Bhagwat, A., et al

    Garcia, A. M., Torrey, P., Bhagwat, A., et al. 2025, arXiv e-prints, arXiv:2503.03804

    2025 arXiv

  22. [30]

    M., Torrey, P., Hemler, Z

    Garcia, A. M., Torrey, P., Hemler, Z. S., et al. 2023, MNRAS, 519, 4716 García-Benito, R., Zibetti, S., Sánchez, S. F., et al. 2015, A&A, 576, A135

    2023

  23. [31]

    R., Shields, G

    Garnett, D. R., Shields, G. A., Skillman, E. D., Sagan, S. P., & Dufour, R. J. 1997, ApJ, 489, 63

    1997

  24. [32]

    K., Pilkington, K., Brook, C

    Gibson, B. K., Pilkington, K., Brook, C. B., Stinson, G. S., & Bailin, J. 2013, A&A, 554, A47

    2013

  25. [33]

    B., Monachesi, A., et al

    Gonzalez-Jara, J., Tissera, P. B., Monachesi, A., et al. 2025, A&A, 693, A282

    2025

  26. [34]

    & Abel, T

    Hahn, O. & Abel, T. 2011, MNRAS, 415, 2101

    2011

  27. [35]

    S., Torrey, P., Qi, J., et al

    Hemler, Z. S., Torrey, P., Qi, J., et al. 2021, MNRAS, 506, 3024

    2021

  28. [36]

    T., Kudritzki, R.-P., Kewley, L

    Ho, I. T., Kudritzki, R.-P., Kewley, L. J., et al. 2015, MNRAS, 448, 2030

    2015

  29. [37]

    T., Seibert, M., Meidt, S

    Ho, I. T., Seibert, M., Meidt, S. E., et al. 2017, ApJ, 846, 39

    2017

  30. [38]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90

    2007

  31. [39]

    & Kobayashi, C

    Ibrahim, D. & Kobayashi, C. 2025, arXiv e-prints, arXiv:2501.11209

    2025

  32. [40]

    1999, ApJS, 125, 439

    Iwamoto, K., Brachwitz, F., Nomoto, K., et al. 1999, ApJS, 125, 439

    1999

  33. [41]

    B., Sillero, E., et al

    Jara-Ferreira, F., Tissera, P. B., Sillero, E., et al. 2024, MNRAS, 530, 1369 Jiménez, N., Tissera, P. B., & Matteucci, F. 2015, ApJ, 810, 137

    2024

  34. [42]

    B., et al

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

    2015

  35. [43]

    2025, ApJ, 978, L39

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

    2025

  36. [44]

    J., Geller, M

    Kewley, L. J., Geller, M. J., & Barton, E. J. 2006, AJ, 131, 2004

    2006

  37. [45]

    J., Rupke, D., Zahid, H

    Kewley, L. J., Rupke, D., Zahid, H. J., Geller, M. J., & Barton, E. J. 2010, ApJ, 721, L48

    2010

  38. [46]

    Khoram, A. H. & Belfiore, F. 2025, A&A, 693, A150

    2025

  39. [47]

    Knollmann, S. R. & Knebe, A. 2009, ApJS, 182, 608 Article number, page 18 Brian Tapia-Contreras et al.: Insight into the physical processes that shape the metallicity profiles in galaxies

    2009

  40. [48]

    T., Blanc, G

    Kreckel, K., Ho, I. T., Blanc, G. A., et al. 2019, ApJ, 887, 80

    2019

  41. [49]

    R., McKee, C

    Krumholz, M. R., McKee, C. F., & Tumlinson, J. 2009, ApJ, 699, 850

    2009

  42. [50]

    K., Walter, F., Brinks, E., et al

    Leroy, A. K., Walter, F., Brinks, E., et al. 2008, AJ, 136, 2782

    2008

  43. [51]

    2022, ApJ, 929, L8

    Li, Z., Wang, X., Cai, Z., et al. 2022, ApJ, 929, L8

    2022

  44. [52]

    F., Feldmann, R., et al

    Ma, X., Hopkins, P. F., Feldmann, R., et al. 2017, MNRAS, 466, 4780

    2017

  45. [53]

    & Mannucci, F

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

    2019

  46. [54]

    & Roy, J.-R

    Martin, P. & Roy, J.-R. 1995, ApJ, 445, 161

    1995

  47. [55]

    2012, Chemical Evolution of Galaxies

    Matteucci, F. 2012, Chemical Evolution of Galaxies

    2012

  48. [56]

    Mihos, J. C. & Hernquist, L. 1996, ApJ, 464, 641

    1996

  49. [57]

    H., Sbordone, L., Rojas-Arriagada, A., et al

    Minniti, J. H., Sbordone, L., Rojas-Arriagada, A., et al. 2020, A&A, 640, A92 Mollá, M., Díaz, Á. I., Cavichia, O., et al. 2019, MNRAS, 482, 3071

    2020

  50. [58]

    B., Tissera, P

    Mosconi, M. B., Tissera, P. B., Lambas, D. G., & Cora, S. A. 2001, MNRAS, 325, 34

    2001

  51. [59]

    & Ostriker, J

    Naab, T. & Ostriker, J. P. 2017, ARA&A, 55, 59

    2017

  52. [60]

    E., Burkhart, B., Wetzel, A., et al

    Orr, M. E., Burkhart, B., Wetzel, A., et al. 2023, MNRAS, 521, 3708

    2023

  53. [61]

    F., Barrera-Ballesteros, J

    Pan, H.-A., Lin, L., Sánchez, S. F., Barrera-Ballesteros, J. K., & Hsieh, B.-C. 2025, ApJ, 982, 130

    2025

  54. [62]

    Pedrosa, S. E. & Tissera, P. B. 2015, A&A, 584, A43

    2015

  55. [63]

    Peeples, M. S. & Shankar, F. 2011, MNRAS, 417, 2962

    2011

  56. [64]

    Perez, J., Michel-Dansac, L., & Tissera, P. B. 2011, MNRAS, 417, 580 Péroux, C. & Howk, J. C. 2020, ARA&A, 58, 363 Péroux, C., Nelson, D., van de V oort, F., et al. 2020, MNRAS, 499, 2462

    2011

  57. [65]

    G., Gibson, B

    Pilkington, K., Few, C. G., Gibson, B. K., et al. 2012, A&A, 540, A56

    2012

  58. [66]

    M., Villata, M., & Navarro, J

    Raiteri, C. M., Villata, M., & Navarro, J. F. 1996, A&A, 315, 105 Rodríguez, S., Garcia Lambas, D., Padilla, N. D., et al. 2022, MNRAS, 514, 6157

    1996

  59. [67]

    V ., Navarro, J

    Sales, L. V ., Navarro, J. F., Theuns, T., et al. 2012, MNRAS, 423, 1544 Sánchez, S. F., Rosales-Ortega, F. F., Iglesias-Páramo, J., et al. 2014, A&A, 563, A49 Sánchez-Menguiano, L., Sánchez, S. F., Pérez, I., et al. 2016, A&A, 587, A70 Sánchez-Menguiano, L., Sánchez, S. F., P...

    2012

  60. [68]

    B., White, S

    Scannapieco, C., Tissera, P. B., White, S. D. M., & Springel, V . 2005, MNRAS, 364, 552

    2005

  61. [69]

    B., White, S

    Scannapieco, C., Tissera, P. B., White, S. D. M., & Springel, V . 2006, MNRAS, 371, 1125

    2006

  62. [70]

    1971, ApJ, 168, 327

    Searle, L. 1971, ApJ, 168, 327

    1971

  63. [71]

    Sen, P. K. 1968, Journal of the American statistical association, 63, 1379

    1968

  64. [72]

    B., Lambas, D

    Sillero, E., Tissera, P. B., Lambas, D. G., & Michel-Dansac, L. 2017, MNRAS, 472, 4404

    2017

  65. [73]

    C., Papovich, C., Momcheva, I., et al

    Simons, R. C., Papovich, C., Momcheva, I., et al. 2021, ApJ, 923, 203

    2021

  66. [74]

    B., & Hernandez-Jimenez, J

    Solar, M., Tissera, P. B., & Hernandez-Jimenez, J. A. 2020, MNRAS, 491, 4894

    2020

  67. [75]

    Somerville, R. S. & Davé, R. 2015, ARA&A, 53, 51

    2015

  68. [76]

    Springel, V ., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726

    2001

  69. [77]

    S., Brook, C., Macciò, A

    Stinson, G. S., Brook, C., Macciò, A. V ., et al. 2013, MNRAS, 428, 129

    2013

  70. [78]

    1950, Indagationes mathematicae, 12, 173

    Theil, H. 1950, Indagationes mathematicae, 12, 173

    1950

  71. [79]

    Tinsley, B. M. 1980, Fund. Cosmic Phys., 5, 287

    1980

  72. [80]

    B., Bignone, L., Gonzalez-Jara, J., et al

    Tissera, P. B., Bignone, L., Gonzalez-Jara, J., et al. 2025, A&A, 697, A134

    2025

  73. [81]

    B., Pedrosa, S

    Tissera, P. B., Pedrosa, S. E., Sillero, E., & Vilchez, J. M. 2016, MNRAS, 456, 2982

    2016

  74. [82]

    B., Rosas-Guevara, Y ., Bower, R

    Tissera, P. B., Rosas-Guevara, Y ., Bower, R. G., et al. 2019, MNRAS, 482, 2208

    2019

  75. [83]

    B., Rosas-Guevara, Y ., Sillero, E., et al

    Tissera, P. B., Rosas-Guevara, Y ., Sillero, E., et al. 2022, MNRAS, 511, 1667

    2022

  76. [84]

    J., Kewley, L., & Hernquist, L

    Torrey, P., Cox, T. J., Kewley, L., & Hernquist, L. 2012, ApJ, 746, 108

    2012

  77. [85]

    2014, A&A, 563, A58

    Troncoso, P., Maiolino, R., Sommariva, V ., et al. 2014, A&A, 563, A58

    2014

  78. [86]

    S., & Werk, J

    Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389

    2017

  79. [87]

    2019, A&A, 624, A141

    Urrutia, T., Wisotzki, L., Kerutt, J., et al. 2019, A&A, 624, A141

    2019

  80. [88]

    2024, MNRAS, 527, 10

    Vallini, L., Witstok, J., Sommovigo, L., et al. 2024, MNRAS, 527, 10

    2024

  81. [89]

    2024, A&A, 691, A19

    Venturi, G., Carniani, S., Parlanti, E., et al. 2024, A&A, 691, A19

    2024

  82. [90]

    Vila-Costas, M. B. & Edmunds, M. G. 1992, MNRAS, 259, 121

    1992

  83. [91]

    & Lilly, S

    Wang, E. & Lilly, S. J. 2022, ApJ, 929, 95

    2022

  84. [92]

    2022, ApJ, 938, L16

    Wang, X., Jones, T., Vulcani, B., et al. 2022, ApJ, 938, L16

    2022

  85. [93]

    A., Treu, T., et al

    Wang, X., Jones, T. A., Treu, T., et al. 2020, ApJ, 900, 183

    2020

  86. [94]

    K., Putman, M

    Werk, J. K., Putman, M. E., Meurer, G. R., & Santiago-Figueroa, N. 2011, ApJ, 735, 71

    2011

  87. [95]

    White, S. D. M. & Rees, M. J. 1978, MNRAS, 183, 341

    1978

  88. [96]

    Woosley, S. E. & Weaver, T. A. 1995, ApJS, 101, 181

    1995

  89. [97]

    2016, ApJ, 827, 74

    Wuyts, E., Wisnioski, E., Fossati, M., et al. 2016, ApJ, 827, 74

    2016

  90. [98]

    M., Henriques, B

    Yates, R. M., Henriques, B. M. B., Fu, J., et al. 2021, MNRAS, 503, 4474

    2021

  91. [99]

    Zahid, H. J. & Bresolin, F. 2011, AJ, 141, 192

    2011

  92. [100]

    Zaritsky, D., Kennicutt, Robert C., J., & Huchra, J. P. 1994, ApJ, 420, 87 Article number, page 19 A&A proofs:manuscript no. aa54013-25 Appendix A: Physical properties ofCIELOgalaxies atz=0. Table A.1: Physical properties and profile characteristics of CIELO galaxies atz=0. Ga...

    1994

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