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Local variations of the radial metallicity gradient in a simulated NIHAO-UHD Milky Way analogue and their implications for (extra-)galactic studies

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Simulated Milky Way's metal gradient is not a straight line.

desk verdict Solid single-simulation study with a real statistical caveat: the non-linear gradient and azimuthal scatter claims likely hold, but the full-sample AIC/BIC is overconfident and the causal language needs softening. read the letter →

arxiv 2412.01157 v2 pith:XMRYKJR5 submitted 2024-12-02 astro-ph.GA

Sven Buder , Tobias Buck , Qian-Hui Chen , Kathryn Grasha This is my paper · ORCID
classification astro-ph.GA
keywords radialmetallicitygradientMilkyWayanalogueNIHAO-UHDsimulationspiralarmschemicalenrichmentironabundancescattergalacticevolutionyoungstellarpopulations
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 uses a high-resolution cosmological simulation of a Milky Way analogue to test whether the radial metallicity gradient of young stars is truly linear. It finds that a straight line is a good first approximation but systematically misses the inner and outer disk, and both a quadratic curve and a piecewise linear fit with a break near 9.3–11.5 kpc describe the data better without one being clearly superior. The scatter in iron abundance grows tenfold from the innermost radii to 20 kpc, driven by stars born at similar times along spiral structures that leave under- and over-enriched streaks of up to 0.2 dex. These local variations imply that radial and azimuthal selection effects can distort gradient measurements in both Galactic and extragalactic studies, and that future surveys should test smooth non-linear gradients, not only broken straight lines.

What carries the argument

The central object is the simulated Milky Way analogue g8.26e11 from the NIHAO-UHD project, a cosmological zoom-in simulation that uses simple-stellar-population tracer particles whose chemical yields are computed with the chempy code. The argument is carried by fitting three functional forms—linear, quadratic, and piecewise linear—to the radial [Fe/H] distribution of young stars, comparing them via residual sums of squares, AIC, and BIC, and then examining the spatial and azimuthal structure of the residuals. The quadratic term, the fitted break radius, and the identification of co-eval stellar streaks on spiral arms are the specific pieces of evidence that support the claims of non-linearity and spiral-driven scatter.

What would settle it

A volume-complete sample of young (<0.5 Gyr) stars in the Milky Way out to 20 kpc that, once selection effects are accounted for, shows a flat median residual from a linear radial fit (no quadratic curvature) and no growth of 1-sigma [Fe/H] scatter with radius would directly contradict the simulation's central predictions. Similarly, high-resolution (≲2 kpc) face-on gas-phase metallicity maps of several nearby spiral galaxies that show no systematic ≈0.1 dex over-enhancement at the trailing edges of spiral arms would falsify the proposed spiral-streak enrichment mechanism.

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Extended reading notes

Core claim

In the inner 20 kpc of the NIHAO-UHD Milky Way analogue, the radial metallicity gradient traced by stars younger than 0.5 Gyr is not purely linear. A quadratic function, with a steeper initial slope of about −0.049 dex kpc⁻¹ that flattens outward by +0.0005 dex kpc⁻², and a piecewise linear function with a break radius at 9.3–11.5 kpc (≈ 2.4–3.0 effective radii) both achieve lower residual sums of squares, AIC, and BIC than a linear fit, and the two non-linear forms fit essentially equally well. The [Fe/H] spread of these young stars rises from about 0.01 dex at 0.25 kpc to 0.06 dex at 8.25 kpc and to 0.10 dex at 19.75 kpc. This scatter is largely caused by stars born at similar times in radial spiral patterns, producing over-enhancements of up to ≈0.1–0.2 dex at trailing spiral edges and under-enhancements at leading edges.

Load-bearing premise

The results depend on the assumption that one cosmological zoom-in simulation, with its particular merger history, weak bar, and adopted chemical yield prescriptions, is representative enough of real Milky Way-like galaxies that its non-linear gradient, scatter growth, and spiral-arm chemical offsets carry general lessons for observations.

Editorial extensions

If this is right

  • Observational studies of the Milky Way that fit only linear or piecewise linear gradients may misinterpret a smooth quadratic flattening as a distinct break radius, since a piecewise linear function can mimic a quadratic across the covered radial range.
  • Young open clusters and other tracers at a given radius should be expected to show intrinsic [Fe/H] scatter of up to ≈0.1 dex at large radii, even with negligible radial migration, solely from spiral-birth patterns.
  • Localized stellar gaps and enriched or depleted streaks found in the simulation imply that sparse or patchy stellar samples can produce spurious gradient features, so volume-complete or carefully selected samples are needed to measure the true gradient shape.
  • Extragalactic IFU observations that resolve scales below about 2 kpc (≈0.5 effective radii) should be able to detect azimuthal chemical offsets around spiral arms, whereas coarser spatial bins will smooth them away.
  • The simulation's outer abundance floor for stellar iron and gas oxygen provides a testable prediction for surveys reaching beyond 2.5 effective radii in Milky Way-mass galaxies.

Reading between the lines

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

  • If real Milky Way-like galaxies behave as this simulation, then the apparent discrepancy between studies that claim a broken gradient and those that claim a smooth one may be a fitting degeneracy rather than a physical dichotomy: the same data can be described equally well by a break or a quadratic flattening, so future work should report both fits and their Bayesian evidence.
  • The predicted correlation between spiral-arm phase and chemical offset could be tested directly with face-on, high-resolution gas-phase metallicity maps of nearby spirals—if the leading/trailing asymmetry of ≈0.1 dex is absent in a large sample, the simulated enrichment-mixing physics would need revision.
  • The tenfold increase in scatter with radius, if generic, implies that any single-radius calibration of the metallicity gradient (e.g., at the solar circle) underestimates chemical inhomogeneity in the outer disk; this would affect interpretations of abundance gradients in dwarf galaxies and low-mass disks where only outer tracers are visible.
  • A natural extension is to track spiral streaks through time in the simulation to distinguish between star formation in pre-enriched gas versus localized self-enrichment; the paper notes this requires a dedicated follow-up.
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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

3 major / 5 minor

Summary. The paper uses one high-resolution NIHAO-UHD cosmological zoom-in simulation of a Milky Way analogue (g8.26e11) to study the shape, scatter, and spatial coherence of the radial metallicity gradient of young stars (age < 0.5 Gyr) and gas within R_Gal <= 20 kpc. After fitting linear, quadratic, and piecewise-linear models (Section 3.1, Eqs. 1-3), it reports that the quadratic and piecewise forms both improve on the linear fit in RSS/AIC/BIC terms (Table 2), with a break radius quoted as 9.3-11.5 kpc; it quantifies the [Fe/H] scatter of young stars rising from ~0.01 dex at 0.25 kpc to ~0.10 dex at 20 kpc; and it attributes the outer-disk scatter to co-eval stellar streaks along spiral features (Figs. 8-9) and to step-like abundance changes at gas spiral edges (Figs. 14-15). Sections 3.3 and 5.5-5.6 discuss how limited radial coverage can make a smoothly flattening gradient appear broken, with implications for Milky Way and extragalactic gradient studies. The paper is transparent about its scope: it is a single-galaxy study with chempy yield prescriptions and SSP tracer particles, and it explicitly notes the limited applicability (Section 6.1) and yield uncertainties (Sections 5.6, 6.2).

Significance. If confirmed, the paper's expectations are observationally actionable: future Milky Way samples should fit quadratic in addition to broken-linear gradients, and the predicted outer-disk scatter of ~0.1 dex and the leading/trailing-edge offsets at gas spiral edges are falsifiable with current IFU surveys and upcoming astrometric-spectroscopic samples. The paper earns credit for shipping reproducible analysis code (Data Availability section), for cross-checking gradient fits with five independent routines (Table 1), for using a profile-likelihood estimate of the break radius, and for explicitly flagging the caveats that bound its generality. Those acknowledged limitations are not the basis of my concerns. The load-bearing weakness is statistical: the full-particle model comparison treats SSP particles as independent, and the binned and full-particle analyses support the non-linearity claim and the break radius to different degrees, so the significance statements in the abstract and conclusions need to be rebuilt on a valid comparison.

major comments (3)
  1. [Section 3.1, Table 2, Eqs. (4)-(6)] The headline claim that a quadratic or piecewise-linear model beats a linear model is based on the full-sample fit to N ~ 34,000 star particles, but Section 2 states that these are SSP tracer particles formed in clustered events, so coeval particles share formation time, birth radius, and initial chemical composition and are not independent measurements. The quoted statistics inherit this problem: the Delta-AIC of roughly 1,900 between linear and quadratic in Table 2, and the parameter uncertainty of 0.00001 on the quadratic coefficient, are inflated by the effective oversampling of correlated particles. The full-particle fit is also dominated by the inner few kpc, where more than half of the young particles reside (Section 3.2) and where the paper itself finds no evidence of non-linearity. The binned comparison is the more honest one, and it is ambiguous in a different way: the binned quadratic coefficient of 0.00031 +/- 0.00024 is individually only about 1.3 sigma, yet the binned RSS improves by a factor of about 3 for both non-linear forms, so support for a non-linear shape exists but needs a valid quantification. Please restate the significance of the non-linearity claim on the binned basis and provide a clustering-corrected analysis of the full sample, for example by fitting at the level of formation events, by resampling star particles, or by estimating an effective number of independent samples.
  2. [Table 2; abstract; Section 5.1] The two estimators of the break radius disagree at a level far larger than their quoted uncertainties: the full-data fit gives R_break = 9.3 +/- 0.1 kpc with the profile-likelihood range extending to 11.5 kpc, while the binned fit gives 11.50 +/- 0.25 kpc. The abstract's 'break radius around 9.3-11.5 kpc', and the comparison to Hemler et al.'s 9 kpc in Section 5.1, therefore overstate the precision of this quantity. The profile-likelihood range is also overconfident because the break radius is optimized on the same correlated data used for the model comparison; the look-elsewhere effect over the grid of tested break radii should be propagated into the uncertainty. The relative ranking of the two non-linear models is likewise unstable: the full-sample AIC prefers the quadratic by about 60, while the binned AIC values for the quadratic and piecewise fits are identical (-240), so the statement that there is no clear preference between them is partly an artifact of combining two inconsistent analyses.
  3. [Sections 4.3-4.4, Figs. 7-9, 14-15] The spiral-born stellar streaks (Groups 1-3) are identified post hoc, and the claims that they drive the outer-disk scatter and produce local over- and under-enhancements of up to +/- 0.2 dex are made without a null test. Please quantify the significance of these features, for example by comparing the [Fe/H] distribution of the streak particles with azimuthally or radially randomized control samples matched in radius and particle number, or by testing whether the density and abundance fluctuations exceed Poisson expectations. The same applies to the step-like gas abundance changes at spiral edges in Figs. 14-15: the slit is selected after the patterns are visible in Fig. 14d ('we convince ourselves of the step-like behaviour by selecting a small slit-like region'), and the reported step amplitudes of about +/- 0.1-0.15 dex need an uncertainty estimate that accounts for this selection.
minor comments (5)
  1. [Section 4, first paragraph] The cross-reference 'we analyse the scatter (Section 4.2), vertical variations (Section 4.2)' lists Section 4.2 twice; the scatter analysis is in Section 4.1.
  2. [Throughout] The text contains numerous typographical artifacts that should be cleaned in the final copyedit, including 'Strae' in the affiliation, '(Y,Y,Z)' for the Cartesian coordinate triple in Section 5.6, the broken glyph in 'Tautvaisien˙e', and the garbled phrase 'the gradient is at least is not purely linear' in Section 5.5.
  3. [Abstract and Section 6.1] The phrase 'volume-complete simulations' is confusing for a single zoom-in run; 'complete within the selected volume' or 'volume-limited' would be more accurate given the R_Gal <= 20 kpc, |z| <= 10 kpc, age < 0.5 Gyr selection described in Section 2.
  4. [Section 3.1] The statement that for R_gal < 10 kpc 'the linear fit performs as well as the other forms' is not quantified anywhere in the paper; please add the corresponding RSS/AIC comparison or soften the claim.
  5. [Section 3.2] The parenthetical '(20 vs. 34000 data points)' is inconsistent with the 40 bins implied by Delta R_Gal = 0.5 kpc over 0-20 kpc; the AIC/BIC values in Table 2 are consistent with N = 40, so please clarify the number of bins used for the binned fits.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports fitted properties of a simulation rather than deriving them from its inputs.

full rationale

The paper is an empirical analysis of an existing cosmological zoom-in simulation. Its central claims (quadratic and piecewise linear fits outperform a linear fit, scatter grows with radius, azimuthal spiral-born streaks create local [Fe/H] deviations) are statements about the simulation output, computed with standard fitting procedures described in Eqs. (1)-(6), and are not derived from a first-principles model whose assumptions already contain the conclusions. No fitted parameter is renamed as a prediction: the break radius, slopes, and scatter are explicitly presented as measured properties of this particular NIHAO-UHD snapshot, with the paper noting that a single spiral galaxy simulation has limited applicability. The self-citations (Buck et al. 2020, 2021 for the simulation and chempy implementation; Buder et al. 2024b for a previous study of the same halo) are infrastructure and reproducibility citations, not load-bearing derivations; the chemical yields are openly stated as adopted inputs, and the paper explicitly acknowledges yield uncertainties. Comparisons to Milky Way and extragalactic observations are contextual and do not define the fitted quantities. The model-comparison statistics involve correlated star particles, but that is a statistical validity concern rather than circularity: the reported preference for non-linear forms is not equivalent to the input by construction, and the binned fits and profile-likelihood range are given alongside the full-sample values. The paper therefore contains no circular step that reduces a claimed result to its own inputs.

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

The ledger lists the fitted model coefficients and selection choices that the conclusions rest on, plus the key domain assumptions: the fidelity of the NIHAO-UHD simulation, the chempy yield prescriptions, and the representativeness of one galaxy. These are not hidden, but they are load-bearing if the results are applied to observations.

free parameters (5)
  • Linear fit slope and intercept (c1, c2) = -0.04109 +/- 0.00005 dex/kpc; 0.46266 +/- 0.00039
    Baseline model in Eq. 1; all non-linearity claims are measured against this fit.
  • Quadratic coefficients (c1, c2, c3) = 0.00045 +/- 0.00001 dex/kpc^2; -0.04864 +/- 0.00018; 0.48031 +/- 0.00055
    Provides the smooth flattening claimed as an alternative to a break radius.
  • Piecewise linear slopes, intercepts, and break radius = inner slope -0.04477 +/- 0.00010, outer slope -0.03562 +/- 0.00014, break 9.3 +/- 0.1 kpc (full); binned break 11.50…
    Used for the alternative broken-line model; the break radius is a fitted quantity, not a directly measured physical feature.
  • Young star age selection threshold = 0.5 Gyr
    Chosen to limit radial migration; all gradient and scatter results apply only to this selected population. The paper tests age sensitivity in Section 5.4 but does not vary this threshold.
  • Radial and vertical selection limits = R_Gal <= 20 kpc; |z| <= 10 kpc (after the age cut, 99% of selected stars are within |z| < 1.4 kpc)
    Defines the analyzed volume; Section 3.3 shows that different radial coverage changes fitted slopes, so this choice directly shapes the results.
assumptions (5)
  • domain assumption NIHAO-UHD zoom-in simulation physics (Gasoline2 hydrodynamics, subgrid turbulent mixing, Stinson feedback) faithfully represents galactic chemical evolution.
    Invoked throughout; Section 2 describes the simulation and Section 6.1 notes the limitation of a single spiral galaxy simulation.
  • domain assumption chempy yield prescriptions and SSP tracer particles produce abundance patterns accurate enough for gradient and scatter analysis.
    Section 2; the authors state in Section 5.6 that uncertainties in enrichment sites, yields, and environments limit the predictive power of absolute abundances.
  • domain assumption One weak-bar, strong-bulge Milky Way analogue is representative of the wider class of star-forming spiral galaxies.
    Section 6.1 acknowledges limited applicability; Section 5.6 suggests expanding to a larger sample of simulated galaxies.
  • standard math AIC and BIC computed with sigma^2 = RSS/N form a valid model comparison procedure.
    Section 3.1, Eqs. 5-6; assumes Gaussian, independent residuals with uniform variance, which the residual density plots only approximately satisfy.
  • domain assumption Young stars younger than 0.5 Gyr trace the current gas-phase metallicity gradient with negligible radial migration.
    Section 2 and Section 5.4; the paper partially tests age dependence but cannot exclude migration even for the youngest stars, citing Frankel et al. 2018.

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Cite this review

Pith. "Pith review of Local variations of the radial metallicity gradient in a simulated NIHAO-UHD Milky Way analogue and their implications for (extra-)galactic studies." pith.science (2026). https://pith.science/paper/XMRYKJR5

@misc{pith2026241201157,
  author       = {Pith},
  title        = {Pith review of: Local variations of the radial metallicity gradient in a simulated NIHAO-UHD Milky Way analogue and their implications for (extra-)galactic studies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XMRYKJR5}},
  note         = {Machine review of arXiv:2412.01157}
}
read the original abstract

Radial metallicity gradients are fundamental to understanding galaxy formation and evolution. In our high-resolution simulation of a NIHAO-UHD Milky Way analogue, we analyze the linearity, scatter, spatial coherence, and age-related variations of metallicity gradients using young stars and gas. While a global linear model generally captures the gradient, it ever so slightly overestimates metallicity in the inner galaxy and underestimates it in the outer regions of our simulated galaxy. Both a quadratic model, showing an initially steeper gradient that smoothly flattens outward, and a piecewise linear model with a break radius around 9.3-11.5~kpc (2.4-3.0 effective radii) fit the data equally better. The spread of [Fe/H] of young stars in the simulation increases by tenfold from the innermost to the outer galaxy at a radius of 20~kpc. We find that stars born at similar times along radial spirals drive this spread in the outer galaxy, with a chemical under- and over-enhancement of up to 0.1 dex at leading and trailing regions of such spirals, respectively. This localised chemical variance highlights the need to examine radial and azimuthal selection effects for both Galactic and extragalactic observational studies. The arguably idealised but volume-complete simulations suggest that future studies should not only test linear and piecewise linear gradients, but also non-linear functions such as quadratic ones to test for a smooth gradient rather than one with a break radius. Either finding would help to determine the importance of different enrichment or mixing pathways and thus our understanding of galaxy formation and evolution scenarios.

Figures

Figures reproduced from arXiv: 2412.01157 by the authors.

Figure 1
Figure 1. — Logarithmic spatial density distribution of stars (upper panels) and gas (lower panels) within 𝑅 < 20 kpc ∼ 5 Re of the NIHAO-UHD Milky Way analogue g8.26e11 in galactocentric cartesian and cylindrical coordinates. Panel c) shows the influence of selecting only young stars with ages below 0.5 Gyr [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. — Face-on view of average simulated metallicity (left panels), a linear radial fit to it (middle panels, see Section 3.1 and Eq. 1) and the fit residuals (right panels) in bins of 0.5 kpc. Shown are metallicity as traced by young star iron abundances (top panels) and gas phase metallicity (bottom) panels [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. — Global fits and deviation to the radial metallicity gradient 𝑅 − [Fe/H]. Functional forms of the linear (red) and quadratic (orange) lines are shown in the legend. Panel a) shows the underlying data of all data points as logarithmic density and the global fit to them as red dashed line. Panel b) shows the deviation of data from a linear gradient as a logarithmic density plot, whereas panel c) shows the 16th and 84… view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: — Deviation of different radial metallicity gradient functions from the global linear fit. Shown are the different functions (linear, quadratic, and piecewise linear) estimated from the full distribution (solid lines) or medians and standard deviations (error bars) in …
Figure 5
Figure 5. Figure 5: — Impact of different coverage in galactocentric radius when fitting a linear radial metallicity gradient to young stars. Each horizontal segment uses a different running radial fitting range between 0.25 and 15 kpc as outlined on the right. For better contrast, the gl…
Figure 6
Figure 6. Figure 6: — Local gradient deviations (red-blue lines) similar to the second lowest row of [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: — Stellar density variation across 8 different sectors (with color-code visualised in panel a) of the radial metallicity gradient 𝑅 − [Fe/H] across 8 different azimuth ranges (panels b-i). A rotating lighthouse-like GIF animation of the median age and median density of…
Figure 8
Figure 8. Figure 8: — Same as [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: — Density distribution (panel a) and age distribution (panel b) of young stars in the azimuthal and radial direction 𝜑Gal. − 𝑅Gal. . In panel a), we also show the groups previously identified in [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: — Comparison of the Milky Way’s radial metallicity trend as traced by Cepheids (black triangles, compiled from literature by Genovali et al. 2014, G+14) as well as young (<0.5 Gyr) open cluster of the Milky Way as traced by the literature compilation from Genovali et …
Figure 11
Figure 11. Figure 11: — Same as [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: — Tracing young stars and gas across galactocentric radii 𝑅Gal. and height 𝑧Gal. across the whole galaxy (panel a) and different azimuthal ranges/sectors (panels b-i). Small rectangles with cool-warm colors along the horizontal axis indicate the local gradient slopes …
Figure 13
Figure 13. Figure 13: — Comparison of the density distribution of young stars and gas in the Milky Way and the NIHAO Milky Way analogue simulation. Panel a) shows the measurements of the Solar vicinity within 5 kpc by Poggio et al. (2021). Panels b) and c) show young stars and gas NIHAO, r…
Figure 14
Figure 14. Figure 14: — Comparison of density distribution of young stars and gas in the NIHAO-UHD Milky Way analogue simulation for the same regions as Figs. 13b and 13c. Panels a) and c) trace median young star Fe and gas O abundances, respectively. Panels b) and d) plot the metallicity …
Figure 15
Figure 15. Figure 15: — Radial gas metallicity gradient of a slit-like region (−2 < 𝑌Gal. / kpc < −1) from [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]
Figure 16
Figure 16. Figure 16: — Stellar density distribution and spread of [Fe/H] across different galactocentric radii with respect to a global linear radial metallicity gradient across different age ranges. Panels a-j) show young stars and exhibit a rather similar trend, whereas the scatter incr…
Figure 17
Figure 17. Figure 17: — Radial metallicity gradients and quadratic fits for different max￾imum age ranges. The quadratic fit to stars below 0.5 Gyr is shown as a dashed red line for reference and the quadratic fit to each shown distribution is overlaid as a solid red line with the function…
Figure 18
Figure 18. Figure 18: — Same as [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]
Figure 19
Figure 19. Figure 19: — Radial metallicity functions for all stars (panel a), young stars (panel b), and gas (panels c and d for iron and oxygen as metallicity tracers) out to 𝑅Gal. ≤ 100 kpc. Panels b and c are comparable to Figs. 3a and 11a for a smaller radial coverage. • We have furthe…

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Works this paper leans on

163 extracted references · 16 canonical work pages

  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]

    Agertz O., et al., 2021, @doi [ ] 10.1093/mnras/stab322 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.5826A 503, 5826

    doi:10.1093/mnras/stab322 2021

  3. [3]

    C., Wilhelm R., Newberg H

    Allende Prieto C., Beers T. C., Wilhelm R., Newberg H. J., Rockosi C. M., Yanny B., Lee Y. S., 2006, @doi [ ] 10.1086/498131 , https://ui.adsabs.harvard.edu/abs/2006ApJ...636..804A 636, 804

    doi:10.1086/498131 2006

  4. [4]

    Anders F., et al., 2014, @doi [ ] 10.1051/0004-6361/201323038 , https://ui.adsabs.harvard.edu/abs/2014A&A...564A.115A 564, A115

    doi:10.1051/0004-6361/201323038 2014

  5. [5]

    Anders F., et al., 2017, @doi [ ] 10.1051/0004-6361/201629363 , https://ui.adsabs.harvard.edu/abs/2017A&A...600A..70A 600, A70

    doi:10.1051/0004-6361/201629363 2017

  6. [6]

    M., et al., 2002a, @doi [ ] 10.1051/0004-6361:20011488 , https://ui.adsabs.harvard.edu/abs/2002A&A...381...32A 381, 32

    Andrievsky S. M., et al., 2002a, @doi [ ] 10.1051/0004-6361:20011488 , https://ui.adsabs.harvard.edu/abs/2002A&A...381...32A 381, 32

    work page doi:10.1051/0004-6361:20011488

  7. [7]

    M., Bersier D., Kovtyukh V

    Andrievsky S. M., Bersier D., Kovtyukh V. V., Luck R. E., Maciel W. J., L \'e pine J. R. D., Beletsky Y. V., 2002b, @doi [ ] 10.1051/0004-6361:20020016 , https://ui.adsabs.harvard.edu/abs/2002A&A...384..140A 384, 140

    work page doi:10.1051/0004-6361:20020016

  8. [8]

    M., Luck R

    Andrievsky S. M., Luck R. E., Martin P., L \'e pine J. R. D., 2004, @doi [ ] 10.1051/0004-6361:20031528 , https://ui.adsabs.harvard.edu/abs/2004A&A...413..159A 413, 159

    work page doi:10.1051/0004-6361:20031528 2004

Show all 163 references
  1. [9]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , http://adsabs.harvard.edu/abs/2013A

    2013 doi

  2. [10]

    Astropy Collaboration et al., 2018, @doi [ ] 10.3847/1538-3881/aabc4f , https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A 156, 123

    2018 doi

  3. [11]

    A., Spitoni E., de Laverny P., Cescutti G., 2025, @doi [ ] 10.1051/0004-6361/202450868 , https://ui.adsabs.harvard.edu/abs/2025A&A...693A...3B 693, A3

    Barbillon M., Recio-Blanco A., Poggio E., Palicio P. A., Spitoni E., de Laverny P., Cescutti G., 2025, @doi [ ] 10.1051/0004-6361/202450868 , https://ui.adsabs.harvard.edu/abs/2025A&A...693A...3B 693, A3

    2025 doi

  4. [12]

    Belfiore F., et al., 2016, @doi [ ] 10.1093/mnras/stw1234 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461.3111B 461, 3111

    2016 doi

  5. [13]

    Belfiore F., et al., 2017, @doi [ ] 10.1093/mnras/stx789 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469..151B 469, 151

    2017 doi

  6. [14]

    A., Wetzel A., Loebman S

    Bellardini M. A., Wetzel A., Loebman S. R., Faucher-Gigu \`e re C.-A., Ma X., Feldmann R., 2021, @doi [ ] 10.1093/mnras/stab1606 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.4586B 505, 4586

    2021 doi

  7. [15]

    A., Wetzel A., Loebman S

    Bellardini M. A., Wetzel A., Loebman S. R., Bailin J., 2022, @doi [ ] 10.1093/mnras/stac1637 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.4270B 514, 4270

    2022 doi

  8. [16]

    S., 2014, @doi [ ] 10.1051/0004-6361/201322631 , http://adsabs.harvard.edu/abs/2014A

    Bensby T., Feltzing S., Oey M. S., 2014, @doi [ ] 10.1051/0004-6361/201322631 , http://adsabs.harvard.edu/abs/2014A

    2014 doi

  9. [17]

    Bergemann M., et al., 2014, @doi [ ] 10.1051/0004-6361/201423456 , http://adsabs.harvard.edu/abs/2014A

    2014 doi

  10. [18]

    Princeton University Press

    Binney J., Tremaine S., 2008, Galactic Dynamics: Second Edition . Princeton University Press

    2008

  11. [19]

    C., Kazantzidis S., Weinberg D

    Bird J. C., Kazantzidis S., Weinberg D. H., Guedes J., Callegari S., Mayer L., Madau P., 2013, @doi [ ] 10.1088/0004-637X/773/1/43 , http://adsabs.harvard.edu/abs/2013ApJ...773...43B 773, 43

    2013 doi

  12. [20]

    Bland-Hawthorn J., Gerhard O., 2016, @doi [ ] 10.1146/annurev-astro-081915-023441 , http://adsabs.harvard.edu/abs/2016ARA

    2016 doi

  13. [21]

    Bland-Hawthorn J., Tepper-Garcia T., Agertz O., Federrath C., 2024, @doi [ ] 10.3847/1538-4357/ad4118 , https://ui.adsabs.harvard.edu/abs/2024ApJ...968...86B 968, 86

    2024 doi

  14. [22]

    Boeche C., et al., 2013, @doi [ ] 10.1051/0004-6361/201322085 , https://ui.adsabs.harvard.edu/abs/2013A&A...559A..59B 559, A59

    2013 doi

  15. [23]

    C., Ryan-Weber E., 2012, @doi [ ] 10.1088/0004-637X/750/2/122 , https://ui.adsabs.harvard.edu/abs/2012ApJ...750..122B 750, 122

    Bresolin F., Kennicutt R. C., Ryan-Weber E., 2012, @doi [ ] 10.1088/0004-637X/750/2/122 , https://ui.adsabs.harvard.edu/abs/2012ApJ...750..122B 750, 122

    2012 doi

  16. [24]

    Buck T., 2020, @doi [ ] 10.1093/mnras/stz3289 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5435B 491, 5435

    2020 doi

  17. [25]

    V., Dutton A

    Buck T., Macci \`o A. V., Dutton A. A., Obreja A., Frings J., 2019, @doi [ ] 10.1093/mnras/sty2913 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.1314B 483, 1314

    2019 doi

  18. [26]

    V., Minchev I., Dutton A

    Buck T., Obreja A., Macci \`o A. V., Minchev I., Dutton A. A., Ostriker J. P., 2020, @doi [ ] 10.1093/mnras/stz3241 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.3461B 491, 3461

    2020 doi

  19. [27]

    V., Pfrommer C., Steinmetz M., Ness M., 2021, @doi [ ] 10.1093/mnras/stab2736 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.3365B 508, 3365

    Buck T., Rybizki J., Buder S., Obreja A., Macci \`o A. V., Pfrommer C., Steinmetz M., Ness M., 2021, @doi [ ] 10.1093/mnras/stab2736 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.3365B 508, 3365

    2021 doi

  20. [28]

    V., 2023, @doi [ ] 10.1093/mnras/stad1503 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.1565B 523, 1565

    Buck T., Obreja A., Ratcliffe B., Lu Y., Minchev I., Macci \`o A. V., 2023, @doi [ ] 10.1093/mnras/stad1503 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.1565B 523, 1565

    2023 doi

  21. [29]

    Buder S., et al., 2019, @doi [ ] 10.1051/0004-6361/201833218 , http://adsabs.harvard.edu/abs/2019A

    2019 doi

  22. [30]

    arXiv:2409.19858

    Buder S., et al., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2409.19858 , https://ui.adsabs.harvard.edu/abs/2024arXiv240919858B p. arXiv:2409.19858

    doi arXiv

  23. [31]

    Buder S., Mijnarends L., Buck T., 2024b, @doi [ ] 10.1093/mnras/stae1552 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.1010B 532, 1010

    doi

  24. [32]

    Cantat-Gaudin T., Anders F., 2020, @doi [ ] 10.1051/0004-6361/201936691 , https://ui.adsabs.harvard.edu/abs/2020A&A...633A..99C 633, A99

    2020 doi

  25. [33]

    Casamiquela L., et al., 2019, @doi [ ] 10.1093/mnras/stz2595 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1821C 490, 1821

    2019 doi

  26. [34]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , https://ui.adsabs.harvard.edu/abs/2003PASP..115..763C 115, 763

    2003 doi

  27. [35]

    J., Kewley L

    Chen Q.-H., Grasha K., Battisti A. J., Kewley L. J., Madore B. F., Seibert M., Rich J. A., Beaton R. L., 2023, @doi [ ] 10.1093/mnras/stac3790 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.4801C 519, 4801

    2023 doi

  28. [36]

    Chen Q.-H., et al., 2024a, @doi [ ] 10.1093/mnras/stad3348 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.2991C 527, 2991

    doi

  29. [37]

    Chen Q.-H., et al., 2024b, @doi [ ] 10.1093/mnras/stae2119 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534..883C 534, 883

    doi

  30. [38]

    Chiappini C., 2002, @doi [ ] 10.1023/A:1019575123561 , https://ui.adsabs.harvard.edu/abs/2002Ap&SS.281..253C 281, 253

    2002 doi

  31. [39]

    Chiappini C., Matteucci F., Gratton R., 1997, @doi [ ] 10.1086/303726 , http://adsabs.harvard.edu/abs/1997ApJ...477..765C 477, 765

    1997 doi

  32. [40]

    Chiappini C., Matteucci F., Romano D., 2001, @doi [ ] 10.1086/321427 , https://ui.adsabs.harvard.edu/#abs/2001ApJ...554.1044C 554, 1044

    2001 doi

  33. [41]

    Chieffi A., Limongi M., 2004, @doi [ ] 10.1086/392523 , 608, 405

    2004 doi

  34. [42]

    Chiosi C., 1980, , https://ui.adsabs.harvard.edu/abs/1980A&A....83..206C 83, 206

    1980

  35. [43]

    Cunha K., et al., 2016, @doi [Astronomische Nachrichten] 10.1002/asna.201612398 , http://adsabs.harvard.edu/abs/2016AN....337..922C 337, 922

    2016 doi

  36. [44]

    N., 2013, @doi [ ] 10.1051/0004-6361/201220539 , https://ui.adsabs.harvard.edu/abs/2013A&A...553A.102D 553, A102

    Di Matteo P., Haywood M., Combes F., Semelin B., Snaith O. N., 2013, @doi [ ] 10.1051/0004-6361/201220539 , https://ui.adsabs.harvard.edu/abs/2013A&A...553A.102D 553, A102

    2013 doi

  37. [45]

    Donor J., et al., 2020, @doi [ ] 10.3847/1538-3881/ab77bc , https://ui.adsabs.harvard.edu/abs/2020AJ....159..199D 159, 199

    2020 doi

  38. [46]

    Foster C., et al., 2021, @doi [ ] 10.1017/pasa.2021.25 , https://ui.adsabs.harvard.edu/abs/2021PASA...38...31F 38, e031

    2021 doi

  39. [47]

    W., 2018, @doi [ ] 10.3847/1538-4357/aadba5 , http://adsabs.harvard.edu/abs/2018ApJ...865...96F 865, 96

    Frankel N., Rix H.-W., Ting Y.-S., Ness M., Hogg D. W., 2018, @doi [ ] 10.3847/1538-4357/aadba5 , http://adsabs.harvard.edu/abs/2018ApJ...865...96F 865, 96

    2018 doi

  40. [48]

    Frankel N., Sanders J., Ting Y.-S., Rix H.-W., 2020, @doi [ ] 10.3847/1538-4357/ab910c , https://ui.adsabs.harvard.edu/abs/2020ApJ...896...15F 896, 15

    2020 doi

  41. [49]

    Fraser-McKelvie A., et al., 2022, @doi [ ] 10.1093/mnras/stab3430 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510..320F 510, 320

    2022 doi

  42. [50]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629272 , http://adsabs.harvard.edu/abs/2016A

    2016 doi

  43. [51]

    M., et al., 2023, @doi [ ] 10.1093/mnras/stac3749 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.4716G 519, 4716

    Garcia A. M., et al., 2023, @doi [ ] 10.1093/mnras/stac3749 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.4716G 519, 4716

    2023 doi

  44. [52]

    Genovali K., et al., 2014, @doi [ ] 10.1051/0004-6361/201323198 , https://ui.adsabs.harvard.edu/abs/2014A&A...566A..37G 566, A37

    2014 doi

  45. [53]

    L., Wetzel A., Bellardini M

    Graf R. L., Wetzel A., Bellardini M. A., Bailin J., 2025, @doi [ ] 10.3847/1538-4357/adacd7 , https://ui.adsabs.harvard.edu/abs/2025ApJ...981...47G 981, 47

    2025 doi

  46. [54]

    Grand R. J. J., Kawata D., Cropper M., 2015, @doi [ ] 10.1093/mnras/stv016 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.4018G 447, 4018

    2015 doi

  47. [55]

    Grand R. J. J., et al., 2016, @doi [ ] 10.1093/mnrasl/slw086 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.460L..94G 460, L94

    2016 doi

  48. [56]

    Grasha K., et al., 2022, @doi [ ] 10.3847/1538-4357/ac5ab2 , https://ui.adsabs.harvard.edu/abs/2022ApJ...929..118G 929, 118

    2022 doi

  49. [57]

    Hackshaw Z., Hawkins K., Filion C., Horta D., Laporte C. F. P., Carr C., Price-Whelan A. M., 2024, @doi [ ] 10.3847/1538-4357/ad900e , https://ui.adsabs.harvard.edu/abs/2024ApJ...977..143H 977, 143

    2024 doi

  50. [58]

    R., et al., 2014, @doi [ ] 10.1088/0004-6256/147/5/116 , https://ui.adsabs.harvard.edu/abs/2014AJ....147..116H 147, 116

    Hayden M. R., et al., 2014, @doi [ ] 10.1088/0004-6256/147/5/116 , https://ui.adsabs.harvard.edu/abs/2014AJ....147..116H 147, 116

    2014 doi

  51. [59]

    R., et al., 2015, @doi [ ] 10.1088/0004-637X/808/2/132 , http://adsabs.harvard.edu/abs/2015ApJ...808..132H 808, 132

    Hayden M. R., et al., 2015, @doi [ ] 10.1088/0004-637X/808/2/132 , http://adsabs.harvard.edu/abs/2015ApJ...808..132H 808, 132

    2015 doi

  52. [60]

    S., et al., 2021, @doi [ ] 10.1093/mnras/stab1803 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.3024H 506, 3024

    Hemler Z. S., et al., 2021, @doi [ ] 10.1093/mnras/stab1803 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.3024H 506, 3024

    2021 doi

  53. [61]

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

    Ho I. T., Kudritzki R.-P., Kewley L. J., Zahid H. J., Dopita M. A., Bresolin F., Rupke D. S. N., 2015, @doi [ ] 10.1093/mnras/stv067 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.2030H 448, 2030

    2015 doi

  54. [62]

    T., et al., 2017, @doi [ ] 10.3847/1538-4357/aa8460 , https://ui.adsabs.harvard.edu/abs/2017ApJ...846...39H 846, 39

    Ho I. T., et al., 2017, @doi [ ] 10.3847/1538-4357/aa8460 , https://ui.adsabs.harvard.edu/abs/2017ApJ...846...39H 846, 39

    2017 doi

  55. [63]

    W., Eilers A.-C., Rix H.-W., 2019, @doi [ ] 10.3847/1538-3881/ab398c , https://ui.adsabs.harvard.edu/abs/2019AJ....158..147H 158, 147

    Hogg D. W., Eilers A.-C., Rix H.-W., 2019, @doi [ ] 10.3847/1538-3881/ab398c , https://ui.adsabs.harvard.edu/abs/2019AJ....158..147H 158, 147

    2019 doi

  56. [64]

    D., 2007, @doi [Comput Sci Eng] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Comput Sci Eng] 10.1109/MCSE.2007.55 , 9, 90

    2007 doi

  57. [65]

    Imig J., et al., 2023, @doi [ ] 10.3847/1538-4357/ace9b8 , https://ui.adsabs.harvard.edu/abs/2023ApJ...954..124I 954, 124

    2023 doi

  58. [66]

    A., 1979, @doi [ ] 10.1086/190568 , https://ui.adsabs.harvard.edu/abs/1979ApJS...39..135J 39, 135

    Janes K. A., 1979, @doi [ ] 10.1086/190568 , https://ui.adsabs.harvard.edu/abs/1979ApJS...39..135J 39, 135

    1979 doi

  59. [67]

    W., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.13256 , https://ui.adsabs.harvard.edu/abs/2024arXiv241013256J p

    Johnson J. W., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.13256 , https://ui.adsabs.harvard.edu/abs/2024arXiv241013256J p. arXiv:2410.13256

    2024 doi arXiv

  60. [68]

    I., Lugaro M., 2016, @doi [ApJ] 10.3847/0004-637X/825/1/26 , 825, 26

    Karakas A. I., Lugaro M., 2016, @doi [ApJ] 10.3847/0004-637X/825/1/26 , 825, 26

    2016 doi

  61. [69]

    Kauffmann G., 1996, @doi [ ] 10.1093/mnras/281.2.475 , https://ui.adsabs.harvard.edu/abs/1996MNRAS.281..475K 281, 475

    1996 doi

  62. [70]

    J., 2023, @doi [ ] 10.1051/0004-6361/202142581 , https://ui.adsabs.harvard.edu/abs/2023A&A...671A..56K 671, A56

    Khoperskov S., Sivkova E., Saburova A., Vasiliev E., Shustov B., Minchev I., Walcher C. J., 2023, @doi [ ] 10.1051/0004-6361/202142581 , https://ui.adsabs.harvard.edu/abs/2023A&A...671A..56K 671, A56

    2023 doi

  63. [71]

    R., Knebe A., 2009, @doi [ ] 10.1088/0067-0049/182/2/608 , https://ui.adsabs.harvard.edu/abs/2009ApJS..182..608K 182, 608

    Knollmann S. R., Knebe A., 2009, @doi [ ] 10.1088/0067-0049/182/2/608 , https://ui.adsabs.harvard.edu/abs/2009ApJS..182..608K 182, 608

    2009 doi

  64. [72]

    A., et al., 2017, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2017arXiv171103234K p

    Kollmeier J. A., et al., 2017, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2017arXiv171103234K p. arXiv:1711.03234

    2017 arXiv

  65. [73]

    Kreckel K., et al., 2019, @doi [ ] 10.3847/1538-4357/ab5115 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887...80K 887, 80

    2019 doi

  66. [74]

    Kreckel K., et al., 2020, @doi [ ] 10.1093/mnras/staa2743 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499..193K 499, 193

    2020 doi

  67. [75]

    R., Ting Y.-S., 2018, @doi [ ] 10.1093/mnras/stx3286 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.2236K 475, 2236

    Krumholz M. R., Ting Y.-S., 2018, @doi [ ] 10.1093/mnras/stx3286 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.2236K 475, 2236

    2018 doi

  68. [76]

    R., Burkhart B., Forbes J

    Krumholz M. R., Burkhart B., Forbes J. C., Crocker R. M., 2018, @doi [ ] 10.1093/mnras/sty852 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.2716K 477, 2716

    2018 doi

  69. [77]

    Kubryk M., Prantzos N., Athanassoula E., 2015, @doi [ ] 10.1051/0004-6361/201424171 , https://ui.adsabs.harvard.edu/abs/2015A&A...580A.126K 580, A126

    2015 doi

  70. [78]

    G., Fall S

    Lacey C. G., Fall S. M., 1985, @doi [ ] 10.1086/162970 , https://ui.adsabs.harvard.edu/abs/1985ApJ...290..154L 290, 154

    1985 doi

  71. [79]

    B., 1976, @doi [ ] 10.1093/mnras/176.1.31 , https://ui.adsabs.harvard.edu/abs/1976MNRAS.176...31L 176, 31

    Larson R. B., 1976, @doi [ ] 10.1093/mnras/176.1.31 , https://ui.adsabs.harvard.edu/abs/1976MNRAS.176...31L 176, 31

    1976 doi

  72. [80]

    Lemasle B., Fran c ois P., Bono G., Mottini M., Primas F., Romaniello M., 2007, @doi [ ] 10.1051/0004-6361:20066375 , https://ui.adsabs.harvard.edu/abs/2007A&A...467..283L 467, 283

    2007 doi

  73. [81]

    D., Primas F., Romaniello M., 2008, @doi [ ] 10.1051/0004-6361:200810192 , https://ui.adsabs.harvard.edu/abs/2008A&A...490..613L 490, 613

    Lemasle B., Fran c ois P., Piersimoni A., Pedicelli S., Bono G., Laney C. D., Primas F., Romaniello M., 2008, @doi [ ] 10.1051/0004-6361:200810192 , https://ui.adsabs.harvard.edu/abs/2008A&A...490..613L 490, 613

    2008 doi

  74. [82]

    Lemasle B., et al., 2013, @doi [ ] 10.1051/0004-6361/201322115 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..31L 558, A31

    2013 doi

  75. [83]

    Lemasle B., et al., 2022, @doi [ ] 10.1051/0004-6361/202243273 , https://ui.adsabs.harvard.edu/abs/2022A&A...668A..40L 668, A40

    2022 doi

  76. [85]

    Li Z., et al., 2024, @doi [ ] 10.1093/mnras/stae480 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.7103L 528, 7103

    2024 doi

  77. [86]

    J., Carollo C

    Lilly S. J., Carollo C. M., Pipino A., Renzini A., Peng Y., 2013, @doi [ ] 10.1088/0004-637X/772/2/119 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772..119L 772, 119

    2013 doi

  78. [87]

    K., 2022, @doi [ ] 10.1093/mnrasl/slac065 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515L..34L 515, L34

    Lu Y., Buck T., Minchev I., Ness M. K., 2022, @doi [ ] 10.1093/mnrasl/slac065 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515L..34L 515, L34

    2022 doi

  79. [88]

    F., Feldmann R., Torrey P., Faucher-Gigu \`e re C.-A., Kere s D., 2017a, @doi [ ] 10.1093/mnras/stx034 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.4780M 466, 4780

    Ma X., Hopkins P. F., Feldmann R., Torrey P., Faucher-Gigu \`e re C.-A., Kere s D., 2017a, @doi [ ] 10.1093/mnras/stx034 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.4780M 466, 4780

    doi

  80. [89]

    F., Wetzel A

    Ma X., Hopkins P. F., Wetzel A. R., Kirby E. N., Angl \'e s-Alc \'a zar D., Faucher-Gigu \`e re C.-A., Kere s D., Quataert E., 2017b, @doi [ ] 10.1093/mnras/stx273 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.467.2430M 467, 2430

    doi

  81. [90]

    Magrini L., Sestito P., Randich S., Galli D., 2009, @doi [ ] 10.1051/0004-6361:200810634 , https://ui.adsabs.harvard.edu/abs/2009A&A...494...95M 494, 95

    2009 doi

  82. [91]

    Magrini L., et al., 2017, @doi [ ] 10.1051/0004-6361/201630294 , https://ui.adsabs.harvard.edu/abs/2017A&A...603A...2M 603, A2

    2017 doi

  83. [92]

    Matteucci F., Recchi S., 2001, @doi [ ] 10.1086/322472 , http://adsabs.harvard.edu/abs/2001ApJ...558..351M 558, 351

    2001 doi

  84. [93]

    Metha B., Trenti M., Chu T., 2021, @doi [ ] 10.1093/mnras/stab2554 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508..489M 508, 489

    2021 doi

  85. [94]

    Minchev I., Famaey B., 2010, @doi [ ] 10.1088/0004-637X/722/1/112 , http://adsabs.harvard.edu/abs/2010ApJ...722..112M 722, 112

    2010 doi

  86. [95]

    Minchev I., Chiappini C., Martig M., 2013, @doi [ ] 10.1051/0004-6361/201220189 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A...9M 558, A9

    2013 doi

  87. [96]

    Minchev I., Chiappini C., Martig M., 2014, @doi [ ] 10.1051/0004-6361/201423487 , https://ui.adsabs.harvard.edu/abs/2014A&A...572A..92M 572, A92

    2014 doi

  88. [97]

    Minchev I., et al., 2018, @doi [ ] 10.1093/mnras/sty2033 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.1645M 481, 1645

    2018 doi

  89. [98]

    M., et al., 2012, @doi [ ] 10.1088/0004-637X/745/1/66 , https://ui.adsabs.harvard.edu/abs/2012ApJ...745...66M 745, 66

    Moran S. M., et al., 2012, @doi [ ] 10.1088/0004-637X/745/1/66 , https://ui.adsabs.harvard.edu/abs/2012ApJ...745...66M 745, 66

    2012 doi

  90. [99]

    Mun M., et al., 2024, @doi [ ] 10.1093/mnras/stae1132 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.5072M 530, 5072

    2024 doi

  91. [100]

    Myers N., et al., 2022, @doi [ ] 10.3847/1538-3881/ac7ce5 , https://ui.adsabs.harvard.edu/abs/2022AJ....164...85M 164, 85

    2022 doi

  92. [101]

    C., Sutherland R

    Nicholls D. C., Sutherland R. S., Dopita M. A., Kewley L. J., Groves B. A., 2017, @doi [ ] 10.1093/mnras/stw3235 , http://adsabs.harvard.edu/abs/2017MNRAS.466.4403N 466, 4403

    2017 doi

  93. [102]

    Obreja A., et al., 2019, @doi [ ] 10.1093/mnras/stz1563 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.4424O 487, 4424

    2019 doi

  94. [103]

    V., 2022, @doi [ ] 10.1051/0004-6361/202140983 , https://ui.adsabs.harvard.edu/abs/2022A&A...657A..15O 657, A15

    Obreja A., Buck T., Macci \`o A. V., 2022, @doi [ ] 10.1051/0004-6361/202140983 , https://ui.adsabs.harvard.edu/abs/2022A&A...657A..15O 657, A15

    2022 doi

  95. [104]

    E., et al., 2023, @doi [ ] 10.1093/mnras/stad676 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.3708O 521, 3708

    Orr M. E., et al., 2023, @doi [ ] 10.1093/mnras/stad676 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.3708O 521, 3708

    2023 doi

  96. [105]

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

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

    1989

  97. [106]

    Palla M., Magrini L., Spitoni E., Matteucci F., Viscasillas V \'a zquez C., Franchini M., Molero M., Randich S., 2024, @doi [ ] 10.1051/0004-6361/202451395 , https://ui.adsabs.harvard.edu/abs/2024A&A...690A.334P 690, A334

    2024 doi

  98. [107]

    Pedregosa F., et al., 2011, J Mach Learn Res, 12, 2825

    2011

  99. [108]

    E., 2007, @doi [Comput Sci Eng] 10.1109/MCSE.2007.53 , 9, 21

    P\'erez F., Granger B. E., 2007, @doi [Comput Sci Eng] 10.1109/MCSE.2007.53 , 9, 21

    2007 doi

  100. [109]

    Perktold J., et al., 2024, statsmodels/statsmodels: Release 0.14.2 , @doi 10.5281/zenodo.593847

    2024 doi

  101. [110]

    Pilkington K., et al., 2012, @doi [ ] 10.1051/0004-6361/201117466 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..56P 540, A56

    2012 doi

  102. [111]

    S., 2003, @doi [ ] 10.1051/0004-6361:20021505 , https://ui.adsabs.harvard.edu/abs/2003A&A...397..109P 397, 109

    Pilyugin L. S., 2003, @doi [ ] 10.1051/0004-6361:20021505 , https://ui.adsabs.harvard.edu/abs/2003A&A...397..109P 397, 109

    2003 doi

  103. [112]

    S., Tautvai s ien \

    Pilyugin L. S., Tautvai s ien \. e G., 2024, @doi [ ] 10.1051/0004-6361/202347032 , https://ui.adsabs.harvard.edu/abs/2024A&A...682A..41P 682, A41

    2024 doi

  104. [113]

    S., Grebel E

    Pilyugin L. S., Grebel E. K., Zinchenko I. A., Nefedyev Y. A., V \' lchez J. M., 2017, @doi [ ] 10.1051/0004-6361/201731256 , https://ui.adsabs.harvard.edu/abs/2017A&A...608A.127P 608, A127

    2017 doi

  105. [114]

    Planck Collaboration et al., 2014, @doi [ ] 10.1051/0004-6361/201321591 , https://ui.adsabs.harvard.edu/abs/2014A&A...571A..16P 571, A16

    2014 doi

  106. [115]

    Poggio E., et al., 2018, @doi [ ] 10.1093/mnrasl/sly148 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481L..21P 481, L21

    2018 doi

  107. [116]

    Poggio E., et al., 2021, @doi [ ] 10.1051/0004-6361/202140687 , https://ui.adsabs.harvard.edu/abs/2021A&A...651A.104P 651, A104

    2021 doi

  108. [117]

    Poggio E., et al., 2022, @doi [ ] 10.1051/0004-6361/202244361 , https://ui.adsabs.harvard.edu/abs/2022A&A...666L...4P 666, L4

    2022 doi

  109. [118]

    S., Woods R., 2013, pynbody: Astrophysics Simulation Analysis for Python , Astrophysics Source Code Library, record ascl:1305.002

    Pontzen A., Ro s kar R., Stinson G. S., Woods R., 2013, pynbody: Astrophysics Simulation Analysis for Python , Astrophysics Source Code Library, record ascl:1305.002

    2013

  110. [119]

    J., Tinsley B

    Quirk W. J., Tinsley B. M., 1973, @doi [ ] 10.1086/151847 , https://ui.adsabs.harvard.edu/abs/1973ApJ...179...69Q 179, 69

    1973 doi

  111. [120]

    L., Ness M

    Ratcliffe B. L., Ness M. K., Buck T., Johnston K. V., Sen B., Beraldo e Silva L., Debattista V. P., 2022, @doi [ ] 10.3847/1538-4357/ac3481 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924...60R 924, 60

    2022 doi

  112. [121]

    Ratcliffe B., et al., 2023, @doi [ ] 10.1093/mnras/stad1573 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.2208R 525, 2208

    2023 doi

  113. [122]

    D., Buck T., Marques L., Lu L., Steinmetz M., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.17326 , https://ui.adsabs.harvard.edu/abs/2024arXiv241017326R p

    Ratcliffe B., Khoperskov S., Minchev I., Lee N. D., Buck T., Marques L., Lu L., Steinmetz M., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.17326 , https://ui.adsabs.harvard.edu/abs/2024arXiv241017326R p. arXiv:2410.17326

    2024 doi arXiv

  114. [123]

    B., 2025, @doi [ ] 10.1051/0004-6361/202452219 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A..56R 694, A56

    Renaud F., Ratcliffe B., Minchev I., Haywood M., Di Matteo P., Agertz O., Romeo A. B., 2025, @doi [ ] 10.1051/0004-6361/202452219 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A..56R 694, A56

    2025 doi

  115. [124]

    Rolleston W. R. J., Smartt S. J., Dufton P. L., Ryans R. S. I., 2000, , https://ui.adsabs.harvard.edu/abs/2000A&A...363..537R 363, 537

    2000

  116. [125]

    F., D \' az A

    Rosales-Ortega F. F., D \' az A. I., Kennicutt R. C., S \'a nchez S. F., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18870.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.415.2439R 415, 2439

    2011

  117. [126]

    Rybizki J., Just A., Rix H.-W., 2017, @doi [ ] 10.1051/0004-6361/201730522 , http://adsabs.harvard.edu/abs/2017A

    2017 doi

  118. [127]

    S \'a nchez-Bl \'a zquez P., et al., 2014, @doi [ ] 10.1051/0004-6361/201423635 , https://ui.adsabs.harvard.edu/abs/2014A&A...570A...6S 570, A6

    2014 doi

  119. [128]

    S \'a nchez-Menguiano L., et al., 2016, @doi [ ] 10.1051/0004-6361/201527450 , https://ui.adsabs.harvard.edu/abs/2016A&A...587A..70S 587, A70

    2016 doi

  120. [129]

    F., et al., 2013, @doi [ ] 10.1051/0004-6361/201220669 , https://ui.adsabs.harvard.edu/abs/2013A&A...554A..58S 554, A58

    S \'a nchez S. F., et al., 2013, @doi [ ] 10.1051/0004-6361/201220669 , https://ui.adsabs.harvard.edu/abs/2013A&A...554A..58S 554, A58

    2013 doi

  121. [130]

    F., et al., 2014, @doi [ ] 10.1051/0004-6361/201322343 , https://ui.adsabs.harvard.edu/abs/2014A&A...563A..49S 563, A49

    S \'a nchez S. F., et al., 2014, @doi [ ] 10.1051/0004-6361/201322343 , https://ui.adsabs.harvard.edu/abs/2014A&A...563A..49S 563, A49

    2014 doi

  122. [131]

    Scarano S., L \'e pine J. R. D., 2013, @doi [ ] 10.1093/mnras/sts048 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428..625S 428, 625

    2013 doi

  123. [133]

    Searle L., 1971, @doi [ ] 10.1086/151090 , https://ui.adsabs.harvard.edu/abs/1971ApJ...168..327S 168, 327

    1971 doi

  124. [134]

    R., et al., 2013, @doi [ ] 10.1093/mnras/sts402 , http://adsabs.harvard.edu/abs/2013MNRAS.429.1156S 429, 1156

    Seitenzahl I. R., et al., 2013, @doi [ ] 10.1093/mnras/sts402 , http://adsabs.harvard.edu/abs/2013MNRAS.429.1156S 429, 1156

    2013 doi

  125. [135]

    R., Wisnioski E., Forbes J

    Sharda P., Krumholz M. R., Wisnioski E., Forbes J. C., Federrath C., Acharyya A., 2021, @doi [ ] 10.1093/mnras/stab252 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.5935S 502, 5935

    2021 doi

  126. [136]

    A., McGee R

    Shaver P. A., McGee R. X., Newton L. M., Danks A. C., Pottasch S. R., 1983, @doi [ ] 10.1093/mnras/204.1.53 , https://ui.adsabs.harvard.edu/abs/1983MNRAS.204...53S 204, 53

    1983 doi

  127. [137]

    Spina L., et al., 2021, @doi [ ] 10.1093/mnras/stab471 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.3279S 503, 3279

    2021 doi

  128. [138]

    Spitoni E., et al., 2023, @doi [ ] 10.1051/0004-6361/202347325 , https://ui.adsabs.harvard.edu/abs/2023A&A...680A..85S 680, A85

    2023 doi

  129. [139]

    Stanghellini L., Magrini L., Casasola V., 2015, @doi [ ] 10.1088/0004-637X/812/1/39 , https://ui.adsabs.harvard.edu/abs/2015ApJ...812...39S 812, 39

    2015 doi

  130. [140]

    Stinson G., Seth A., Katz N., Wadsley J., Governato F., Quinn T., 2006, @doi [ ] 10.1111/j.1365-2966.2006.11097.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.373.1074S 373, 1074

    2006

  131. [141]

    S., Brook C., Macci \`o A

    Stinson G. S., Brook C., Macci \`o A. V., Wadsley J., Quinn T. R., Couchman H. M. P., 2013, @doi [ ] 10.1093/mnras/sts028 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428..129S 428, 129

    2013 doi

  132. [142]

    arXiv:2409.09290

    Sun X., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2409.09290 , https://ui.adsabs.harvard.edu/abs/2024arXiv240909290S p. arXiv:2409.09290

    2024 doi arXiv

  133. [143]

    B., 2005, ASPC, http://adsabs.harvard.edu/abs/2005ASPC..347...29T 347, 29

    Taylor M. B., 2005, ASPC, http://adsabs.harvard.edu/abs/2005ASPC..347...29T 347, 29

    2005

  134. [144]

    Tepper-Garc \' a T., Bland-Hawthorn J., Vasiliev E., Agertz O., Teyssier R., Federrath C., 2024, @doi [ ] 10.1093/mnras/stae2372 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535..187T 535, 187

    2024 doi

  135. [145]

    M., 1980, , https://ui.adsabs.harvard.edu/abs/1980FCPh....5..287T 5, 287

    Tinsley B. M., 1980, , https://ui.adsabs.harvard.edu/abs/1980FCPh....5..287T 5, 287

    1980

  136. [146]

    B., Rosas-Guevara Y., Bower R

    Tissera P. B., Rosas-Guevara Y., Bower R. G., Crain R. A., del P Lagos C., Schaller M., Schaye J., Theuns T., 2019, @doi [ ] 10.1093/mnras/sty2817 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.2208T 482, 2208

    2019 doi

  137. [147]

    Tuntipong S., et al., 2024, @doi [ ] 10.1093/mnras/stae2042 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.4334T 533, 4334

    2024 doi

  138. [148]

    A., 1980, @doi [ ] 10.1086/158460 , http://adsabs.harvard.edu/abs/1980ApJ...242..242T 242, 242

    Twarog B. A., 1980, @doi [ ] 10.1086/158460 , http://adsabs.harvard.edu/abs/1980ApJ...242..242T 242, 242

    1980 doi

  139. [149]

    A., Ashman K

    Twarog B. A., Ashman K. M., Anthony-Twarog B. J., 1997, @doi [ ] 10.1086/118667 , https://ui.adsabs.harvard.edu/abs/1997AJ....114.2556T 114, 2556

    1997 doi

  140. [150]

    J., Shen J., Li Z.-Y., 2021, @doi [ ] 10.3847/1538-4357/ac27a9 , https://ui.adsabs.harvard.edu/abs/2021ApJ...922..189V 922, 189

    Vickers J. J., Shen J., Li Z.-Y., 2021, @doi [ ] 10.3847/1538-4357/ac27a9 , https://ui.adsabs.harvard.edu/abs/2021ApJ...922..189V 922, 189

    2021 doi

  141. [151]

    M., Esteban C., 1996, @doi [ ] 10.1093/mnras/280.3.720 , https://ui.adsabs.harvard.edu/abs/1996MNRAS.280..720V 280, 720

    Vilchez J. M., Esteban C., 1996, @doi [ ] 10.1093/mnras/280.3.720 , https://ui.adsabs.harvard.edu/abs/1996MNRAS.280..720V 280, 720

    1996 doi

  142. [152]

    Vincenzo F., Matteucci F., Belfiore F., Maiolino R., 2016, @doi [ ] 10.1093/mnras/stv2598 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.4183V 455, 4183

    2016 doi

  143. [153]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

    2020 doi

  144. [154]

    W., Keller B

    Wadsley J. W., Keller B. W., Quinn T. R., 2017, @doi [ ] 10.1093/mnras/stx1643 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.2357W 471, 2357

    2017 doi

  145. [155]

    Walt S. v. d., Colbert S. C., Varoquaux G., 2011, @doi [Comput Sci Eng] 10.1109/MCSE.2011.37 , 13, 22

    2011 doi

  146. [156]

    A., Stinson G

    Wang L., Dutton A. A., Stinson G. S., Macci \`o A. V., Penzo C., Kang X., Keller B. W., Wadsley J., 2015, @doi [ ] 10.1093/mnras/stv1937 , http://adsabs.harvard.edu/abs/2015MNRAS.454...83W 454, 83

    2015 doi

  147. [157]

    Willett E., et al., 2023, @doi [ ] 10.1093/mnras/stad2374 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.2141W 526, 2141

    2023 doi

  148. [158]

    J., Bureau M., Cappellari M., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15582.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.400.1665W 400, 1665

    Williams M. J., Bureau M., Cappellari M., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15582.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.400.1665W 400, 1665

    2009

  149. [159]

    Wyse R. F. G., Silk J., 1989, @doi [ ] 10.1086/167329 , https://ui.adsabs.harvard.edu/abs/1989ApJ...339..700W 339, 700

    1989 doi

  150. [160]

    W., Friel E

    Yong D., Carney B. W., Friel E. D., 2012, @doi [ ] 10.1088/0004-6256/144/4/95 , https://ui.adsabs.harvard.edu/abs/2012AJ....144...95Y 144, 95

    2012 doi

  151. [161]

    Zari E., Hashemi H., Brown A. G. A., Jardine K., de Zeeuw P. T., 2018, @doi [ ] 10.1051/0004-6361/201834150 , https://ui.adsabs.harvard.edu/abs/2018A&A...620A.172Z 620, A172

    2018 doi

  152. [162]

    W., Frankel N., Xiang M., Poggio E., Drimmel R., Tkachenko A., 2021, @doi [ ] 10.1051/0004-6361/202039726 , https://ui.adsabs.harvard.edu/abs/2021A&A...650A.112Z 650, A112

    Zari E., Rix H. W., Frankel N., Xiang M., Poggio E., Drimmel R., Tkachenko A., 2021, @doi [ ] 10.1051/0004-6361/202039726 , https://ui.adsabs.harvard.edu/abs/2021A&A...650A.112Z 650, A112

    2021 doi

  153. [163]

    J., Huchra J

    Zaritsky D., Kennicutt Robert C. J., Huchra J. P., 1994, @doi [ ] 10.1086/173544 , https://ui.adsabs.harvard.edu/abs/1994ApJ...420...87Z 420, 87

    1994 doi

  154. [164]

    R., 2025, @doi [ ] 10.1093/mnras/staf194 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.tmp..198Z

    Zhang C., Li Z., Hu Z., Krumholz M. R., 2025, @doi [ ] 10.1093/mnras/staf194 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.tmp..198Z

    2025 doi

  155. [165]

    B., Martig M., Hayden M

    van de Sande J., Fraser-McKelvie A., Fisher D. B., Martig M., Hayden M. R., the GECKOS Survey collaboration 2023, @doi [arXiv e-prints] 10.48550/arXiv.2306.00059 , https://ui.adsabs.harvard.edu/abs/2023arXiv230600059V p. arXiv:2306.00059

    2023 doi arXiv

Pith tools

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