Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- I. A golden thread through time and space

T0 review · 3 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Pith's one-line read A simulated Milky Way analogue shows that stars born in the core of its last major merger end up more tightly bound and more metal-rich than stars from the outskirts, preserving a 'golden thread' between birth place and present-day orbit.

desk verdict A genuinely useful cosmological test of the Skúladóttir/Mori stripping scenario with honest caveats, but the headline gradient is never derived in the body and the highest-energy bin is contaminated — worth reviewing, not desk-rejectable. read the letter →

arxiv 2510.11284 v2 pith:4S4CSL7A submitted 2025-10-13 astro-ph.GA

classification astro-ph.GA
keywords galacticarchaeologygalaxymergerschemodynamicalmemorystellarpopulationsorbitalenergybirthradiusMilkyWayanaloguecosmologicalzoom-insimulation
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

Using a high-resolution cosmological zoom-in simulation with traced star birth positions, the paper aims to show that a major galaxy merger does not erase the connection between where a star was born and how it moves and what it is made of today. The central result is that stars born in the infalling galaxy's core are now on more tightly bound orbits and are more chemically enriched, while stars from its outskirts are less bound and more metal-poor, with a measured gradient of about -0.05 dex in iron abundance per kiloparsec of birth radius. This chemodynamical memory also appears in elemental abundance planes, and it survives in the simulated analogue of the Milky Way's last major merger roughly 8-10 Gyr ago. A second claim is that standard selections of accreted stars by orbital energy and angular momentum capture only about 42% of the accreted population, systematically missing the metal-rich core; therefore current reconstructions are biased towards the metal-poor outskirts. If correct, present-day orbits and chemistry can be used to reconstruct the internal structure of disrupted satellite galaxies.

What carries the argument

The argument is carried by three elements. First, birth-position tracing in 100 Myr time steps gives a clean, simulation-side classification of stars as in-situ, previously accreted, or currently accreting, without relying on orbital cuts. Second, the specific orbital energy E (per unit mass) and the radial action J_R, computed for every star particle, serve as the dynamical memory variables: the paper divides accreted stars into energy quartiles and shows that median chemistry, present-day radius, and birth radius all change with E. Third, the 'golden thread' - a spline-interpolated path through the progenitor's star-forming regions over time - visualises the accretion geometry and connects

What would settle it

A single calculation would settle it: repeat the birth-position tracing and energy sorting with different threshold choices, or on a different Milky Way analogue with no major merger, and check whether the energy-[Fe/H] gradient and the roughly 42% completeness number persist. If the gradient reverses or vanishes when the classification cut is varied, or if a galaxy without a major merger shows the same apparent gradient due to in-situ contamination, the central claim is falsified. Observationally, a targeted search for very metal-rich accreted stars ([Fe/H] above about -0.5) in the inner few

Watch

Extended reading notes

Core claim

The paper's central claim is that chemodynamical memory survives a major merger. Sorting stars accreted in the simulated last major merger by their present-day specific orbital energy into four quartiles, the authors find a monotonic trend: the most bound (lowest-energy) stars were born in the innermost regions of the progenitor, now lie within a few kiloparsecs of the centre, and have the highest [Fe/H]; the least bound stars were born in the outskirts and are most metal-poor. They interpret this as a stripping-from-outside-in process: outer stars were lost first onto higher-energy orbits, while the core arrived late and sank to the centre. Quantitatively they measure d[Fe/H]/dR_birth' ~ -0

Load-bearing premise

The classification of stars as 'previously accreted' depends on hand-tuned thresholds in the birth-position cut (birth radius greater than 50 kpc or |z_birth| greater than 5 kpc, combined with negative energy); if that cut misassigns stars born in the progenitor core during the final merger stages, the inferred energy-metallicity correlation could be partly an artifact of the selection.

Editorial extensions

If this is right

  • Present-day orbital energy can serve as a proxy for birth radius inside the disrupted progenitor, so the internal structure of the accreted galaxy is partially recoverable from phase-space data.
  • Observational samples selected by integrals of motion are incomplete by about 58% in the simulation, meaning reported metallicities and inferred masses for the last major merger are biased toward metal-poor, low-mass values.
  • Metallicity preselection in the inner Galaxy, e.g. [Fe/H] below -0.5, can raise the accretion recovery fraction from about 2% to 10-30%, guiding future survey strategies.
  • The energy dependence of [Fe/H] implies the progenitor had a radial metallicity gradient, i.e. it formed stars from the inside out before disruption.
  • Mass estimates derived from the mass-metallicity relation shift by up to a factor of about 40 depending on which energy zone is sampled, which can explain part of the offset between surviving and disrupted dwarf galaxies.

Reading between the lines

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

  • If the same memory operates in real Milky Way mergers, the most metal-rich accreted stars ([Fe/H] above about -0.5) should be concentrated in the bulge or inner Galaxy, and targeted searches there would be a sharper test than Solar-neighbourhood samples.
  • The 42% completeness figure comes from one simulated merger; if it varies systematically with merger mass ratio or orbit, calibrating this bias across multiple simulations would let observers correct observed masses of the last major merger.
  • The highest-energy quartile's up-to-50% contamination from unrelated small accretion events suggests that single-event reconstructions in the outer halo will be noisy; chemical tagging may be needed to isolate the major-merger stars.
  • A direct extension would be to measure d[Fe/H]/dR_birth' in other simulated mergers to test whether the -0.05 dex/kpc slope is a universal feature or is set by the progenitor's own star-formation efficiency gradient.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper uses a high-resolution NIHAO-UHD cosmological zoom-in simulation of a Milky Way analogue to test whether stars accreted in the last major merger retain a chemodynamical memory of their birth location within the progenitor. Tracing stellar birth positions in 100 Myr steps and computing present-day orbital energies, the authors report that stars born in the progenitor's core are now more tightly bound and more metal-rich, while stars born in the outskirts are less bound and more metal-poor, in line with the scenario of Skúladóttir et al. (2025). They additionally quantify the incompleteness of standard integrals-of-motion selections (finding ~42% completeness) and argue that such selections miss the chemically enriched core, biasing progenitor reconstructions. The paper presents qualitative support in Figs. 6, 8, and 9, and discusses implications for mass-metallicity relations and future inner-Galaxy surveys.

Significance. If the qualitative finding is robust, it provides an independent cosmological confirmation of a scenario previously inferred from idealized simulations and small stellar samples, and it carries practical implications for designing observational searches for GSE core stars. The paper's strengths include genuine 100 Myr birth-position tracing, a high-resolution fully cosmological setup, public analysis code and data, and explicit disclosure of several caveats. The selection-efficiency analysis (42%/28% completeness) is a useful, concrete contribution even if the quantitative gradient claim were removed. However, the quantitative headline gradient is not derived in the body, and the high-energy, metal-poor leg of the correlation is acknowledged to be contaminated by other accretion events, so the central quantitative claim is not yet established.

major comments (3)
  1. [Abstract / Sec. 3.3] The abstract (as supplied) advertises d[Fe/H]/dR_birth' ≈ -0.05 dex/kpc as a headline quantitative result. No such fit appears anywhere in the body; R_birth' is not defined, and no uncertainties, fitting method, or sample are given. The full-text abstract omits this number. Because this is the only quantitative measure of the central 'memory' claim and is vulnerable to the contamination issue below, it must either be derived with a well-defined estimator or removed from the abstract.
  2. [Sec. 3.3.1, Fig. 6] Sec. 3.3.1 states that in the highest-energy zone, comparing the overall past-accretion selection with stars following the golden thread yields contamination of up to 50% from other accretion events. This zone is precisely the metal-poor, high-energy leg of the trend in Fig. 6. Since small disrupting dwarfs are themselves metal-poor and on high-energy orbits, the energy-metallicity gradient could be partly produced or steepened by contamination. The paper does not report a contamination-corrected or golden-thread-only gradient. Because the clean sample is already constructed via Table A1, the analysis should be repeated on that sample and the sensitivity quantified before the correlation is presented as a property of the last major merger.
  3. [Sec. 3.1, Eq. (5)] The past-accretion selection in Eq. (5) uses R_birth,3D > 50 kpc or |z_birth| > 5 kpc together with E < 0. These thresholds are hand-tuned and are not restricted to the major merger; the paper's conclusions about 'the progenitor' assume that this sample is dominated by the GSE analogue. The authors validate broad purity (95% unbound for ongoing, 99% bound for past) but do not quantify contamination of the energy-quartile analysis by the other mergers visible in Fig. 1b. This matters because the energy quartiles are defined over this mixed sample. A quantitative decomposition by progenitor (golden-thread vs. others) should be provided, or the text should be reworded to refer to 'the accreted population' rather than 'the progenitor' where the mixed selection is used.
minor comments (4)
  1. [Throughout] Stray '/gtb' tokens appear in several figure captions and text passages (e.g. 'See/gtbfor individual figures', 'Table A1/gtb'). These proofing artifacts should be removed.
  2. [Sec. 3.1, Eq. (4)] The units in Eq. (4) are inconsistent: energy thresholds are given in 'kpc km s−1' while Eq. (3) and Table 1 use 'km^2 s^-2' (e.g. −0.58×10^5 kpc km s−1 vs. −0.58×10^5 km^2 s^-2). Please make units consistent throughout.
  3. [Sec. 2.1] Typo: 'build-tin tools' should be 'built-in tools'.
  4. [References / Sec. 5] The references to Naidu et al. (2022a) and (2022b) are used inconsistently; the text should clearly distinguish the arXiv paper from the ApJ paper and use consistent citation labels.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the simulation-based correlations are not forced by the selection or by self-citation.

full rationale

The paper's central claims—that accreted stars born in the progenitor's core are more tightly bound and more metal-rich, while those from the outskirts are less bound and more metal-poor—are empirical results from a cosmological zoom-in simulation with independent birth-position tracing. The 'past accretion' selection (Eq. 5) uses birth-radius thresholds (R_birth,3D > 50 kpc or |z_birth| > 5 kpc) to separate accreted from in-situ stars, but this binary classification does not by construction impose the continuous [Fe/H]-versus-energy trend seen in Fig. 6 or the energy-birth-radius correlation in Fig. 9. The 'golden thread' clean sample (Table A1) is selected by spatial and age boxes, not by metallicity or energy, so its use to illustrate the chemodynamical memory is not definitional. The scenario of Skúladóttir et al. (2025) is an external observational hypothesis being tested, not a fitted input; the self-citation is not load-bearing because the simulation results stand independently. The abstract's quantitative gradient d[Fe/H]/dR_birth' ≈ -0.05 dex/kpc is not derived in the body, but an omitted derivation is a support problem, not circularity. The acknowledged up-to-50% contamination in the highest-energy zone (Sec. 3.3.1) is a validity caveat about selection completeness, not a tautology: the contamination weakens but does not reduce the claim to its inputs. Overall, no step in the derivation chain is equivalent to its inputs by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim relies on the simulation's sub-grid chemical model, the birth-position tracing cadence, and the representativeness of one cosmological zoom-in. The paper inherits the simulation's yield and feedback choices rather than deriving them; the only hand-tuned inputs introduced here are the classification thresholds and energy bins.

free parameters (3)
  • Birth-radius and height thresholds in past-accretion selection (Eq. 5) = R_birth,3D > 50 kpc; |z_birth| > 5 kpc
    Chosen by hand and 'optimised' against the age-metallicity plane (Sec. 3.1); these cuts define the accreted sample whose properties are then interpreted as merger memory.
  • Ongoing-accretion criteria (Eq. 4) = R3D>50 kpc or E>0 or (age<10 Gyr and [Fe/H]<-1)
    Ad hoc selection to include younger stars of infalling dwarfs; affects denominator and contamination estimates.
  • Energy quartile boundaries = -0.37, -0.58, -0.77 10^5 km^2 s^-2
    Four zones chosen to have ~25% of accreted stars each; the magnitude of reported trends depends on these bins.
assumptions (5)
  • domain assumption NIHAO-UHD sub-grid physics (star formation, feedback, turbulent diffusion) and Chempy enrichment with adopted yields reproduce the relevant chemodynamical trends.
    Used throughout; paper notes absolute abundances and dispersions are smaller than observed and should not be over-interpreted (Sec. 3.3.2), so this assumption is partially load-bearing.
  • domain assumption Birth positions traced at 100 Myr cadence capture each star's formation site.
    Sec. 2.2; authors note potential inaccuracies around the time of the major merger (footnote 1).
  • domain assumption Simulation g8.26e11 is a representative analogue for the Milky Way's last major merger.
    Sec. 2.1; only one galaxy is analysed, no ensemble variance.
  • domain assumption Stäckel fudge action finder (agama) provides adequate integrals of motion.
    Sec. 2.3; approximate but standard method for orbit classification.
  • domain assumption The 50 kpc boundary separates the main galaxy from infalling satellites.
    Used in Eqs. 4-6 and throughout; arbitrary but conventional.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- I. A golden thread through time and space." pith.science (2026). https://pith.science/paper/4S4CSL7A

@misc{pith2026251011284,
  author       = {Pith},
  title        = {Pith review of: The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- I. A golden thread through time and space},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4S4CSL7A}},
  note         = {Machine review of arXiv:2510.11284}
}
abstract

Understanding how past major mergers shaped the Milky Way's present-day structure is a key goal of Galactic archaeology. The Galaxy's chemical and dynamical structure retains the imprint of such events, including a major accretion episode around 8-10 Gyr ago. Recent findings suggest that present-day orbital energy correlates with stellar chemistry and birth location within the merging progenitor galaxy. Using a high-resolution NIHAO-UHD cosmological zoom-in simulation of a Milky Way analogue, we trace the birth positions, ages, and present-day orbits of stars accreted in its last major merger. We show that stars born in the progenitor's core are more tightly bound to the Milky Way and more chemically enriched, while those from the outskirts are less bound and more metal-poor. This supports the Sk\'ulad\'ottir et al. (2025) scenario that accreted progenitor stars of different chemistry were deposited onto different orbital energies as the galaxy was stripped from the outside in, now in a cosmological context. Quantitatively, we measure a metallicity gradient with progenitor birth radius of $\mathrm{d[Fe/H]}/\mathrm{d}R_\mathrm{birth}^\prime \approx -0.05\,\mathrm{dex\,kpc^{-1}}$, demonstrating that abundance patterns retain measurable memory of formation location within the disrupted satellite. This chemodynamical memory is also evident in elemental planes such as [Al/Fe] vs. [Mg/Mn], consistent with gradients in progenitor star formation efficiency. We further show that common integrals-of-motion selections systematically miss stars from the chemically enriched core, biasing reconstructions toward the metal-poor outskirts. Together, our results demonstrate that chemodynamical memory survives the merger and can reconstruct the accreted galaxy's internal structure, while highlighting biases in current selections of accreted stars.

Figures

Figures reproduced from arXiv: 2510.11284 by the authors.

Figure 1
Figure 1. Tracing in-situ stars (blue) alongside past (red) and ongoing (purple) accretion components, shown in their present-day positions (panel a) and birth (panel b) positions (in 100 Myr intervals). The red overdensities in panel b primarily reflect the same accreted galaxy observed at different epochs ‡. 2 SIMULATED DATA In this study we examine the high-resolution cosmological zoom-in simulation g8.26e11, a Milky Way a… view at source ↗
Figure 2
Figure 2. Age–metallicity distributions of in-situ (blue), previously accreted (red), and currently accreting (purple) stars, also shown as marginal 2D histograms with age (top) and [Fe/H] (right). A vertical dashed line indicates the time of the major merger around 8.6 Gyr ago ‡. Amiga Halo Finder (Knollmann & Knebe 2009), using the build-tin tools of the pynbody package (Pontzen et al. 2013). To orient the system, we rotate… view at source ↗
Figure 3
Figure 3. Birth positions in different Galactocentric planes of all star particles that are now within 50 kpc (grey density scale), where those born in-situ are in blue and those of the last major merger in red. Birth positions are estimated in 100 Myr steps and thus allow us to follow the changing position of star formation of the now accreted galaxy with respect to the Milky Way analogue. A dark golden line then interpolate… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Orbit properties of in-situ formed stars (blue, left panels), past accretion (red, middle panels), and ongoing accretion (purple, right panels). Both the specific energy 𝐸 (upper panels) and the radial action 𝐽𝑅 (lower panels) are shown with the angular momentum 𝐽𝜑 ≡ 𝐿…
Figure 5
Figure 5. Figure 5: Angular momentum 𝐽𝜑 ≡ 𝐿𝑍 vs. Specific energy 𝐸 (top panel) and radial action 𝐽𝑅 for stars (bottom panel) for stars within 𝑅3D < 50 kpc at redshift 𝑧 = 0. Bins are coloured by the fraction of previously accreted stars (defined as Figs. 4b/(a+b) and 4d/(d+e), respectivel…
Figure 6
Figure 6. Figure 6: Histograms of [Fe/H] distributions of the whole galaxy (grey), and its previously accreted stars with different orbit energies as given in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Abundance distributions of (past) accreted stars in blue, while the whole simulation is shown in the background (black). Yellow, orange, light and dark red dashed lines show the median abundances for 5-percent-bins if the x-axis of accreted stars for the different ener…
Figure 8
Figure 8. Figure 8: Present-day spatial distribution of accreted stars in the Galactocentric 𝑋-𝑌 plane. Panels a-d) show the density distribution of accreted stars with highest to lowest orbit energy quartiles, where the dashed circle shows a solar-analogue 𝑅2D = 8.2 kpc. Panel legends li…
Figure 9
Figure 9. Figure 9: Birth positions (determined in batches of 100 Myr) coloured by present-day orbit energy, both before the major merger (top, for star formation between 11.65 and 10.25 Gyr ago) and just before and during the merger (bottom, for star formation between 10.15 and 8.55 Gyr …
Figure 10
Figure 10. Figure 10: Histograms of [Fe/H] distribution for accreted stars with different present-day galactocentric radii 𝑅2D with median [Fe/H] indicated in the legend for a each region ‡. stars in the Solar neighbourhood by Nissen & Schuster (2010), but mainly those with 𝐸 > −0.57 × 105…
Figure 11
Figure 11. Figure 11: Metallicity distribution functions (relative to total number of stars in the galaxy). Top panel shows the distribution of the inner 𝑅3D < 3.6 kpc and how in-situ (blue) and accreted populations (golden) contribute to it. Panel b) is showing the same golden distributio…
Figure 12
Figure 12. Figure 12: Mass-metallicity relations (dashed lines) for different galaxies and selections. We list both reported measurements of disrupted (black star symbols), surviving (grey squares), and irregular dwarf galaxies (grey circles) by Kirby et al. (2013) and Naidu et al. (2022a)…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- II. Were Splash stars heated or already born hot?

    astro-ph.GA 2025-10 conditional novelty 6.0 of 10

    In a NIHAO-UHD Milky Way analogue, Splash-like stars were already born on dynamically hot orbits; the last major merger did not significantly heat them.

Reference graph

Works this paper leans on

118 extracted references · 10 canonical work pages · cited by 1 Pith paper

  1. [1]

    Abdurro'uf et al., 2022, @doi [ ] 10.3847/1538-4365/ac4414 , https://ui.adsabs.harvard.edu/abs/2022ApJS..259...35A 259, 35

  2. [2]

    S., et al., 2021, @doi [ ] 10.3847/2041-8213/abdbb8 , https://ui.adsabs.harvard.edu/abs/2021ApJ...908L...8A 908, L8

    Aguado D. S., et al., 2021, @doi [ ] 10.3847/2041-8213/abdbb8 , https://ui.adsabs.harvard.edu/abs/2021ApJ...908L...8A 908, L8

  3. [3]

    Arentsen A., et al., 2020a, @doi [ ] 10.1093/mnrasl/slz156 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491L..11A 491, L11

  4. [4]

    Arentsen A., et al., 2020b, @doi [ ] 10.1093/mnras/staa1661 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496.4964A 496, 4964

  5. [5]

    J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , http://adsabs.harvard.edu/abs/2009ARA

    Asplund M., Grevesse N., Sauval A. J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , http://adsabs.harvard.edu/abs/2009ARA

  6. [6]

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

  7. [7]

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

  8. [8]

    Barbuy B., Chiappini C., Gerhard O., 2018, @doi [ ] 10.1146/annurev-astro-081817-051826 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..223B 56, 223

Show all 118 references
  1. [9]

    Belokurov V., Kravtsov A., 2022, @doi [ ] 10.1093/mnras/stac1267 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514..689B 514, 689

  2. [10]

    Belokurov V., et al., 2006, @doi [ ] 10.1086/504797 , http://adsabs.harvard.edu/abs/2006ApJ...642L.137B 642, L137

  3. [11]

    W., Koposov S

    Belokurov V., Erkal D., Evans N. W., Koposov S. E., Deason A. J., 2018, @doi [ ] 10.1093/mnras/sty982 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478..611B 478, 611

  4. [12]

    L., Fattahi A., Smith M

    Belokurov V., Sanders J. L., Fattahi A., Smith M. C., Deason A. J., Evans N. W., Grand R. J. J., 2020, @doi [ ] 10.1093/mnras/staa876 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.3880B 494, 3880

  5. [13]

    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

  6. [14]

    Bensby T., et al., 2017, @doi [ ] 10.1051/0004-6361/201730560 , http://adsabs.harvard.edu/abs/2017A

  7. [15]

    Bensby T., et al., 2019, @doi [The Messenger] 10.18727/0722-6691/5123 , https://ui.adsabs.harvard.edu/abs/2019Msngr.175...35B 175, 35

  8. [16]

    Binney J., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21757.x , http://adsabs.harvard.edu/abs/2012MNRAS.426.1324B 426, 1324

  9. [17]

    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

  10. [18]

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

  11. [19]

    B., Kawata D., Gibson B

    Brook C. B., Kawata D., Gibson B. K., Freeman K. C., 2004, @doi [ ] 10.1086/422709 , https://ui.adsabs.harvard.edu/abs/2004ApJ...612..894B 612, 894

  12. [20]

    Brown A. G. A., 2021, @doi [ ] 10.1146/annurev-astro-112320-035628 , https://ui.adsabs.harvard.edu/abs/2021ARA&A..59...59B 59, 59

  13. [21]

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

  14. [22]

    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

  15. [23]

    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

  16. [24]

    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

  17. [25]

    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

  18. [26]

    H., Buder S., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.02456 , https://ui.adsabs.harvard.edu/abs/2025arXiv250302456B p

    Buck T., G \"u nes B., Viterbo G., Oliver W. H., Buder S., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.02456 , https://ui.adsabs.harvard.edu/abs/2025arXiv250302456B p. arXiv:2503.02456

  19. [27]

    Buder S., et al., 2022, @doi [ ] 10.1093/mnras/stab3504 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.2407B 510, 2407

  20. [28]

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

  21. [29]

    Buder S., Buck T., Chen Q.-H., Grasha K., 2025, @doi [ ] 10.33232/001c.137295 , https://ui.adsabs.harvard.edu/abs/2025OJAp....8E..47B 8, 47

  22. [30]

    J., Katz H., Rey M

    Cameron A. J., Katz H., Rey M. P., Saxena A., 2023, @doi [ ] 10.1093/mnras/stad1579 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.3516C 523, 3516

  23. [31]

    J., Fattahi A., Callingham T

    Carrillo A., Deason A. J., Fattahi A., Callingham T. M., Grand R. J. J., 2024, @doi [ ] 10.1093/mnras/stad3274 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.2165C 527, 2165

  24. [32]

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

  25. [33]

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

  26. [34]

    Chiappini C., et al., 2019, @doi [The Messenger] 10.18727/0722-6691/5122 , https://ui.adsabs.harvard.edu/abs/2019Msngr.175...30C 175, 30

  27. [35]

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

  28. [36]

    P., Zaritsky D., Bonaca A., Cargile P., Johnson B

    Conroy C., Naidu R. P., Zaritsky D., Bonaca A., Cargile P., Johnson B. D., Caldwell N., 2019, @doi [ ] 10.3847/1538-4357/ab5710 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887..237C 887, 237

  29. [37]

    S., et al., 2019, @doi [ ] 10.1093/mnras/stz2550 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5900D 489, 5900

    Da Costa G. S., et al., 2019, @doi [ ] 10.1093/mnras/stz2550 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5900D 489, 5900

  30. [38]

    Das P., Hawkins K., Jofr \'e P., 2020, @doi [ ] 10.1093/mnras/stz3537 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.5195D 493, 5195

  31. [39]

    M., Sanders J

    Dillamore A. M., Sanders J. L., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2506.09117 , https://ui.adsabs.harvard.edu/abs/2025arXiv250609117D p. arXiv:2506.09117

  32. [40]

    M., Helmi A., Matsuno T., Ruiz-Lara T., Balbinot E., L \"o vdal S., 2023, @doi [ ] 10.1051/0004-6361/202244546 , https://ui.adsabs.harvard.edu/abs/2023A&A...670L...2D 670, L2

    Dodd E., Callingham T. M., Helmi A., Matsuno T., Ruiz-Lara T., Balbinot E., L \"o vdal S., 2023, @doi [ ] 10.1051/0004-6361/202244546 , https://ui.adsabs.harvard.edu/abs/2023A&A...670L...2D 670, L2

  33. [41]

    arXiv:2509.06773

    Ernandes H., Sk \'u lad \'o ttir \'A ., Feltzing S., Feuillet D., 2025, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2025arXiv250906773E p. arXiv:2509.06773

  34. [42]

    K., Frankel N., Lind K., Frinchaboy P

    Feuillet D. K., Frankel N., Lind K., Frinchaboy P. M., Garc \' a-Hern \'a ndez D. A., Lane R. R., Nitschelm C., Roman-Lopes A. r., 2019, @doi [ ] 10.1093/mnras/stz2221 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.1742F 489, 1742

  35. [43]

    K., Feltzing S., Sahlholdt C

    Feuillet D. K., Feltzing S., Sahlholdt C. L., Casagrande L., 2020, @doi [ ] 10.1093/mnras/staa1888 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497..109F 497, 109

  36. [44]

    K., Sahlholdt C

    Feuillet D. K., Sahlholdt C. L., Feltzing S., Casagrande L., 2021, @doi [ ] 10.1093/mnras/stab2614 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.1489F 508, 1489

  37. [45]

    Freeman K., Bland-Hawthorn J., 2002, @doi [ ] 10.1146/annurev.astro.40.060401.093840 , http://adsabs.harvard.edu/abs/2002ARA

  38. [46]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629512 , http://adsabs.harvard.edu/abs/2016A

  39. [47]

    Gaia Collaboration et al., 2018, @doi [ ] 10.1051/0004-6361/201833051 , http://adsabs.harvard.edu/abs/2018A

  40. [48]

    Gallazzi A., Charlot S., Brinchmann J., White S. D. M., Tremonti C. A., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09321.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.362...41G 362, 41

  41. [49]

    Gilmore G., Reid N., 1983, @doi [ ] 10.1093/mnras/202.4.1025 , http://adsabs.harvard.edu/abs/1983MNRAS.202.1025G 202, 1025

  42. [50]

    Grand R. J. J., et al., 2018, @doi [ ] 10.1093/mnras/stx3025 , http://adsabs.harvard.edu/abs/2018MNRAS.474.3629G 474, 3629

  43. [51]

    arXiv:2507.05060

    Gunes B., Buder S., Buck T., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2507.05060 , https://ui.adsabs.harvard.edu/abs/2025arXiv250705060G p. arXiv:2507.05060

  44. [52]

    Hawkins K., Jofr \'e P., Masseron T., Gilmore G., 2015, @doi [ ] 10.1093/mnras/stv1586 , http://adsabs.harvard.edu/abs/2015MNRAS.453..758H 453, 758

  45. [53]

    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

  46. [54]

    Helmi A., 2020, @doi [ ] 10.1146/annurev-astro-032620-021917 , https://ui.adsabs.harvard.edu/abs/2020ARA&A..58..205H 58, 205

  47. [55]

    H., Massari D., Veljanoski J., Brown A

    Helmi A., Babusiaux C., Koppelman H. H., Massari D., Veljanoski J., Brown A. G. A., 2018, @doi [ ] 10.1038/s41586-018-0625-x , http://adsabs.harvard.edu/abs/2018Natur.563...85H 563, 85

  48. [56]

    Horta D., et al., 2021, @doi [ ] 10.1093/mnras/staa2987 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1385H 500, 1385

  49. [57]

    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

  50. [58]

    arXiv:2505.12505

    Ji X., Belokurov V., Maiolino R., Monty S., Isobe Y., Kravtsov A., McClymont W., \"U bler H., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2505.12505 , https://ui.adsabs.harvard.edu/abs/2025arXiv250512505J p. arXiv:2505.12505

  51. [59]

    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

  52. [60]

    Katz D., et al., 2023, @doi [ ] 10.1051/0004-6361/202244220 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...5K 674, A5

  53. [61]

    N., Cohen J

    Kirby E. N., Cohen J. G., Guhathakurta P., Cheng L., Bullock J. S., Gallazzi A., 2013, @doi [ ] 10.1088/0004-637X/779/2/102 , https://ui.adsabs.harvard.edu/abs/2013ApJ...779..102K 779, 102

  54. [62]

    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

  55. [63]

    H., Helmi A., Massari D., Price-Whelan A

    Koppelman H. H., Helmi A., Massari D., Price-Whelan A. M., Starkenburg T. K., 2019, @doi [ ] 10.1051/0004-6361/201936738 , https://ui.adsabs.harvard.edu/abs/2019A&A...631L...9K 631, L9

  56. [64]

    Kunder A., et al., 2025, @doi [ ] 10.3847/1538-3881/adefdd , https://ui.adsabs.harvard.edu/abs/2025AJ....170..173K 170, 173

  57. [65]

    Lucey M., et al., 2019, @doi [ ] 10.1093/mnras/stz1847 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2283L 488, 2283

  58. [66]

    Lucey M., et al., 2022, @doi [ ] 10.1093/mnras/stab2878 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509..122L 509, 122

  59. [67]

    F., et al., 2024, @doi [ ] 10.1051/0004-6361/202347633 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A.115M 692, A115

    Martin N. F., et al., 2024, @doi [ ] 10.1051/0004-6361/202347633 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A.115M 692, A115

  60. [68]

    Matsuno T., Hirai Y., Tarumi Y., Hotokezaka K., Tanaka M., Helmi A., 2021, @doi [ ] 10.1051/0004-6361/202040227 , https://ui.adsabs.harvard.edu/abs/2021A&A...650A.110M 650, A110

  61. [69]

    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

  62. [70]

    Monty S., et al., 2024, @doi [ ] 10.1093/mnras/stae1895 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.2420M 533, 2420

  63. [71]

    Mori A., Di Matteo P., Salvadori S., Khoperskov S., Pagnini G., Haywood M., 2024, @doi [ ] 10.1051/0004-6361/202449291 , https://ui.adsabs.harvard.edu/abs/2024A&A...690A.136M 690, A136

  64. [72]

    C., Evans N

    Myeong G. C., Evans N. W., Belokurov V., Amorisco N. C., Koposov S. E., 2018, @doi [ ] 10.1093/mnras/stx3262 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.1537M 475, 1537

  65. [73]

    C., Vasiliev E., Iorio G., Evans N

    Myeong G. C., Vasiliev E., Iorio G., Evans N. W., Belokurov V., 2019, @doi [ ] 10.1093/mnras/stz1770 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.1235M 488, 1235

  66. [74]

    P., Conroy C., Bonaca A., Johnson B

    Naidu R. P., Conroy C., Bonaca A., Johnson B. D., Ting Y.-S., Caldwell N., Zaritsky D., Cargile P. A., 2020, @doi [ ] 10.3847/1538-4357/abaef4 , https://ui.adsabs.harvard.edu/abs/2020ApJ...901...48N 901, 48

  67. [75]

    P., et al., 2021, @doi [ ] 10.3847/1538-4357/ac2d2d , https://ui.adsabs.harvard.edu/abs/2021ApJ...923...92N 923, 92

    Naidu R. P., et al., 2021, @doi [ ] 10.3847/1538-4357/ac2d2d , https://ui.adsabs.harvard.edu/abs/2021ApJ...923...92N 923, 92

  68. [76]

    P., et al., 2022a, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2022arXiv220409057N p

    Naidu R. P., et al., 2022a, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2022arXiv220409057N p. arXiv:2204.09057

  69. [77]

    P., et al., 2022b, @doi [ ] 10.3847/2041-8213/ac5589 , https://ui.adsabs.harvard.edu/abs/2022ApJ...926L..36N 926, L36

    Naidu R. P., et al., 2022b, @doi [ ] 10.3847/2041-8213/ac5589 , https://ui.adsabs.harvard.edu/abs/2022ApJ...926L..36N 926, L36

  70. [78]

    Ness M., et al., 2013a, @doi [ ] 10.1093/mnras/sts629 , http://adsabs.harvard.edu/abs/2013MNRAS.430..836N 430, 836

  71. [79]

    Ness M., et al., 2013b, @doi [ ] 10.1093/mnras/stt533 , http://adsabs.harvard.edu/abs/2013MNRAS.432.2092N 432, 2092

  72. [80]

    E., Schuster W

    Nissen P. E., Schuster W. J., 2010, @doi [ ] 10.1051/0004-6361/200913877 , http://adsabs.harvard.edu/abs/2010A

  73. [81]

    E., Amarsi A

    Nissen P. E., Amarsi A. M., Sk \'u lad \'o ttir \'A ., Schuster W. J., 2024, @doi [ ] 10.1051/0004-6361/202348392 , https://ui.adsabs.harvard.edu/abs/2024A&A...682A.116N 682, A116

  74. [82]

    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

  75. [83]

    Orkney M. D. A., Laporte C. F. P., Grand R. J. J., Springel V., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2506.07038 , https://ui.adsabs.harvard.edu/abs/2025arXiv250607038O p. arXiv:2506.07038

  76. [84]

    P., Frebel A., Naidu R

    Ou X., Ji A. P., Frebel A., Naidu R. P., Limberg G., 2024, @doi [ ] 10.3847/1538-4357/ad6f9b , https://ui.adsabs.harvard.edu/abs/2024ApJ...974..232O 974, 232

  77. [85]

    W., Mehta R., Shen C., Vogelstein J

    Panda S., Palaniappan S., Xiong J., Bridgeford E. W., Mehta R., Shen C., Vogelstein J. T., 2019, @doi [arXiv e-prints] 10.48550/arXiv.1907.02088 , https://ui.adsabs.harvard.edu/abs/2019arXiv190702088P p. arXiv:1907.02088

  78. [86]

    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

  79. [87]

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

  80. [88]

    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

  81. [89]

    Portail M., Wegg C., Gerhard O., Ness M., 2017, @doi [ ] 10.1093/mnras/stx1293 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.1233P 470, 1233

  82. [90]

    Queiroz A. B. A., et al., 2023, @doi [ ] 10.1051/0004-6361/202245399 , https://ui.adsabs.harvard.edu/abs/2023A&A...673A.155Q 673, A155

  83. [91]

    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

  84. [92]

    Ratcliffe B., et al., 2025, @doi [ ] 10.1051/0004-6361/202452658 , https://ui.adsabs.harvard.edu/abs/2025A&A...698A.267R 698, A267

  85. [93]

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

  86. [94]

    L., Binney J., 2015, @doi [ ] 10.1093/mnras/stu2598 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.2479S 447, 2479

    Sanders J. L., Binney J., 2015, @doi [ ] 10.1093/mnras/stu2598 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.2479S 447, 2479

  87. [95]

    A., et al., 2021, @doi [ ] 10.3847/1538-3881/ac2cbc , https://ui.adsabs.harvard.edu/abs/2021AJ....162..303S 162, 303

    Santana F. A., et al., 2021, @doi [ ] 10.3847/1538-3881/ac2cbc , https://ui.adsabs.harvard.edu/abs/2021AJ....162..303S 162, 303

  88. [96]

    Sch \"o nrich R., Binney J., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15365.x , http://adsabs.harvard.edu/abs/2009MNRAS.399.1145S 399, 1145

  89. [97]

    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

  90. [98]

    P., Rudie G

    Senchyna P., Plat A., Stark D. P., Rudie G. C., Berg D., Charlot S., James B. L., Mingozzi M., 2024, @doi [ ] 10.3847/1538-4357/ad235e , https://ui.adsabs.harvard.edu/abs/2024ApJ...966...92S 966, 92

  91. [99]

    Sestito F., et al., 2021, @doi [ ] 10.1093/mnras/staa3479 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.3750S 500, 3750

  92. [100]

    R., Bland-Hawthorn J., 2021, @doi [ ] 10.1093/mnras/stab2015 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.5882S 507, 5882

    Sharma S., Hayden M. R., Bland-Hawthorn J., 2021, @doi [ ] 10.1093/mnras/stab2015 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.5882S 507, 5882

  93. [101]

    K., Mori A., Feltzing S., Lucchesi R

    Sk \'u lad \'o ttir \'A ., Ernandes H., Feuillet D. K., Mori A., Feltzing S., Lucchesi R. E. R., Di Matteo P., 2025, @doi [ ] 10.3847/2041-8213/addc66 , https://ui.adsabs.harvard.edu/abs/2025ApJ...986L..21S 986, L21

  94. [102]

    D., Combes F., Katz D., G \'o mez A., 2015, @doi [ ] 10.1051/0004-6361/201424281 , https://ui.adsabs.harvard.edu/abs/2015A&A...578A..87S 578, A87

    Snaith O., Haywood M., Di Matteo P., Lehnert M. D., Combes F., Katz D., G \'o mez A., 2015, @doi [ ] 10.1051/0004-6361/201424281 , https://ui.adsabs.harvard.edu/abs/2015A&A...578A..87S 578, A87

  95. [103]

    R., 2010, @doi [ ] 10.1146/annurev-astro-081309-130806 , http://adsabs.harvard.edu/abs/2010ARA

    Soderblom D. R., 2010, @doi [ ] 10.1146/annurev-astro-081309-130806 , http://adsabs.harvard.edu/abs/2010ARA

  96. [104]

    Spitoni E., Silva Aguirre V., Matteucci F., Calura F., Grisoni V., 2019, @doi [ ] 10.1051/0004-6361/201834188 , https://ui.adsabs.harvard.edu/abs/2019A&A...623A..60S 623, A60

  97. [105]

    Starkenburg E., et al., 2017, @doi [ ] 10.1093/mnras/stx1068 , http://adsabs.harvard.edu/abs/2017MNRAS.471.2587S 471, 2587

  98. [106]

    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

  99. [107]

    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

  100. [108]

    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

  101. [109]

    H., Coronado J., Rix H.-W., 2019, @doi [ ] 10.1093/mnras/stz209 , http://adsabs.harvard.edu/abs/2019MNRAS.484.3291T 484, 3291

    Trick W. H., Coronado J., Rix H.-W., 2019, @doi [ ] 10.1093/mnras/stz209 , http://adsabs.harvard.edu/abs/2019MNRAS.484.3291T 484, 3291

  102. [110]

    Vasiliev E., 2019, @doi [ ] 10.1093/mnras/sty2672 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.1525V 482, 1525

  103. [111]

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

  104. [112]

    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

  105. [113]

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

  106. [114]

    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

  107. [115]

    Yoshii Y., 1982, , http://adsabs.harvard.edu/abs/1982PASJ...34..365Y 34, 365

  108. [116]

    Yuan Z., et al., 2020, @doi [ ] 10.3847/1538-4357/ab6ef7 , https://ui.adsabs.harvard.edu/abs/2020ApJ...891...39Y 891, 39

  109. [117]

    R., Smiljanic R., 2023, @doi [ ] 10.1051/0004-6361/202347229 , https://ui.adsabs.harvard.edu/abs/2023A&A...677A..74D 677, A74

    da Silva A. R., Smiljanic R., 2023, @doi [ ] 10.1051/0004-6361/202347229 , https://ui.adsabs.harvard.edu/abs/2023A&A...677A..74D 677, A74

  110. [118]

    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.stat...

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.