Pith. sign in

REVIEW 3 major objections 5 minor 164 references

Simulations of the accreted stellar halos of low-mass field galaxies

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Low-mass field galaxies are predicted to have faint, compact accreted stellar halos that overtake the in-situ stars only around 15 kiloparsecs, at surface densities of $10^{3.5}$ to $10^6$ solar masses per square kiloparsec.

desk verdict Solid extension of particle tagging to low-mass halos; testable predictions, but the transition-radius headline rides on a spherical-in-situ assumption the paper itself flags. read the letter →

arxiv 2501.13317 v2 pith:AXJUF23L submitted 2025-01-23 astro-ph.GA astro-ph.CO

Andrew P. Cooper (1) , Carlos S. Frenk (2) , Wojciech A. Hellwing (3) , Sownak Bose (2) ((1) National Tsing Hua University , Taiwan (2) Durham University (3) Center for Theoretical Physics , Polish Academy of Sciences) This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords stellarhalosdwarfgalaxiesaccretedmassparticletaggingsemi-analyticgalaxyformationN-bodycosmologicalsimulationlowsurfacebrightnessM33analogues
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper sets out quantitative predictions for the faint, diffuse outer stellar halos of field dwarf galaxies — the component built from stars ripped out of smaller satellite galaxies as they merge — across host dark-matter masses from $10^8$ to $10^{12}$ solar masses. The authors combine the GALFORM semi-analytic model of galaxy formation with the COCO cosmological N-body simulation and the STINGS particle-tagging technique, which lets each stellar generation inherit the phase-space distribution of the most-bound dark matter in its halo. The claims are concrete: in the least massive halos that can form stars the accreted fraction is below ten per cent; accreted stars follow profiles as compact as, or more compact than, the in-situ body, so accretion rarely builds bulges in dwarfs; and in M33-like galaxies the accreted surface density crosses that of the in-situ stars at about 15 kiloparsecs, at $10^{3.5}$ to $10^6$ solar masses per square kiloparsec. If these numbers are right, ultra-deep imaging surveys know how faint a dwarf stellar halo must be to be detectable, and a predicted population of 'failed Milky Ways' — $10^{12}$-solar-mass halos with exceptionally inefficient star formation — becomes a count-up target among M33-stellar-mass field galaxies.

What carries the argument

The central mechanism is the STINGS particle-tagging method, in which every single-age stellar population formed by the semi-analytic model receives its own set of $N$-body particles drawn from the most-bound fraction $f_{\rm mb}=3$ per cent of its host dark-matter halo, ranked by binding energy and given equal stellar weight. Because tagged particles diffuse in phase space over time, each population relaxes into a near-exponential surface-density profile whose scale is set by the mass and concentration of the halo at formation, with no baryonic back-reaction on the potential. This one-parameter construction converts the GALFORM merger trees in the COCO simulation into roughly 6400 resolved central galaxies above $10^{10}$-solar-mass halos, and tags every star with its formation time and its branch of the merger tree, so in-situ and accreted stars are separated cleanly. The value of $f_{\rm mb}$ is calibrated against the observed size-mass relation of low-mass galaxies, and the paper shows the average profile of the accreted component is insensitive to its value.

What would settle it

Image a few dozen isolated M33-like field galaxies at surface brightnesses near 30 magnitudes per square arcsecond (roughly $10^3$ solar masses per square kiloparsec). If the radius at which an extended, accreted component overtakes the in-situ body is systematically outside 10 to 25 kiloparsecs, or the crossover surface density lies outside the predicted range $10^{3.5}$ to $10^6$ solar masses per square kiloparsec at matched virial mass, the tagging prescription fails at this mass scale. An independent check is the 'failed Milky Way' population: deep surveys of field galaxies around $10^{12}$-solar-mass halos should uncover an excess of M33-stellar-mass galaxies with anomalously bright accreted halos, and not finding that excess would falsify the model's star-formation efficiency at this halo mass.

Watch

Extended reading notes

Core claim

The paper claims that at fixed stellar mass the stellar halos of low-mass field galaxies are far more diverse than at fixed virial mass, and that the properties of the accreted component are governed by the virial mass of the host. Its headline result is at M33-like stellar masses: the surface density of accreted stars exceeds that of the in-situ component at roughly 15 kiloparsecs, at a level $3.5 \lesssim \log_{10} M_\star/{\rm M}_\odot\,{\rm kpc}^{-2} \lesssim 6$ that varies systematically with virial mass. Around this scale the paper further claims that the scale lengths of accreted halos are smaller than those of the in-situ disk yet the accreted component remains diffuse rather than bulge-like; that in halos below about $10^{11}$ solar masses the accreted density is lower than the in-situ density at every radius accessible to integrated-light observations; and that below roughly $10^8$ solar masses of stars the fraction of galaxies with no significant accreted component rises toward half of the population. It also advances a population statement: a small but significant subset of $\sim 10^{12}$-solar-mass halos hosts 'failed Milky Ways', galaxies with M33-like stellar mass and exceptionally inefficient star formation whose comparatively bright accreted halos make their abundance measurable by low-surface-brightness surveys.

Load-bearing premise

The load-bearing assumption is that each new generation of stars simply copies the orbits of the most-bound 3 per cent of its halo's dark-matter particles at birth, with no extra gravity from the galaxy itself, so the in-situ component comes out spherical rather than disk-like and satellite disruption is driven by dark matter alone.

Editorial extensions

If this is right

  • Ultra-deep surveys of M33-mass field galaxies should find the accreted component overtaking the in-situ component near 15 kiloparsecs, with the crossover surface density tied systematically to virial mass.
  • Below virial masses of about $10^{11}$ solar masses the accreted halo should be effectively undetectable in integrated light at every radius accessible to current surveys.
  • Classical bulges built by accretion should be rare below Milky Way mass, so observed spheroids in dwarf galaxies should trace in-situ formation (pseudo-bulges) rather than merger-built components.
  • Near $10^9$ solar masses of stars, halos assembled from several comparable progenitors become rare, and below $10^8$ solar masses the fraction of galaxies with no significant accreted component rises to roughly half at $10^7$ solar masses.
  • The average metallicity measured at about 30 kiloparsecs should recover the mass of the most massive accreted progenitor to within roughly a factor of ten, with scatter growing toward lower galaxy mass.

Reading between the lines

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

  • A scale-free version of the paper's central test is implied by its own figures: the radius where accreted stars overtake the in-situ body, divided by the in-situ half-mass radius, varies strongly across the simulated population, so measuring that ratio — which needs no virial masses — would discriminate this model from prescriptions that tie the halo transition to the disk scale length.
  • The model's neglect of baryonic gravity cuts asymmetrically at this mass scale: the claim that accreted halos are invisible at observable radii in the lowest-mass halos is the fragile half (a disk potential would shred satellites more aggressively than the collisionless treatment), whereas the M33-scale crossover numbers are the robust half, because they are set by dark-matter assembly that the ta
  • The 'failed Milky Way' population converts a galaxy-formation question into a counting experiment: with a few hundred ultra-deep fields of M33-like dwarfs, the measured space density of galaxies with anomalously bright accreted halos would either confirm or rule out the star-formation efficiency at fixed halo mass assumed by the semi-analytic model used here.
  • Passing the released star-particle data through a mock-image pipeline that applies a real survey's surface-brightness limits, pixel scale, and sky noise would upgrade the paper's qualitative 'observable halo' statements into countable detection fractions — a direct route from these predictions to a survey strategy.
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 / 5 minor

Summary. This paper uses the GALFORM semi-analytic galaxy formation model applied to the COCO zoom N-body simulation, together with the STINGS particle-tagging technique, to predict the properties of accreted stellar halos in field galaxies with present-day virial masses between 10^8 and 10^12 solar masses. After calibrating the single free tagging parameter f_mb = 3 per cent against the observed size-mass relation, the authors compare their model to the SMHM relation, TNG, Auriga, and observational data for M33 and other low-mass galaxies. The main quantitative claims are that accreted mass fractions are typically below 10 per cent in the least massive star-forming halos, that the radial scale lengths of accreted halos are comparable to or smaller than the in situ components at low masses, and that at the M33 stellar-mass scale the accreted surface density exceeds the in situ surface density at about 15 kpc, with surface densities in the range 3.5 < log10(M_star/Msun kpc^-2) < 6. The paper also highlights a population of 'failed Milky Ways' and makes a subset of the stellar particle data publicly available.

Significance. If the quantitative predictions hold, this paper provides a useful and testable extension of stellar-halo modelling to the dwarf-galaxy regime, a range that has been comparatively little explored with self-consistent cosmological methods. The headline prediction of a transition radius around 15 kpc and the associated surface-density range is falsifiable with current and upcoming ultra-low-surface-brightness surveys, and the comparison with M33 data is a genuinely useful synthesis. The paper's strengths include its explicit validation against the SMHM relation, size-mass relation, Auriga, TNG, and Dragonfly data; its transparent discussion of resolution limits and model dependence; and the public release of stellar particle data. The principal weakness is that the quantitative transition-radius prediction rests on a single value of f_mb calibrated to sizes and on a spherical exponential in-situ component that is acknowledged to be an oversimplification; the sensitivity of the quoted numbers to these assumptions is not quantified.

major comments (3)
  1. [Sections 2.3.3, 5.1.1, 5.1.2; conclusion (v)] The headline prediction of a transition radius near 15 kpc and the quoted surface-density range is computed using in-situ profiles that are spherical exponentials by construction (Section 2.3.3), and the paper itself acknowledges in Section 5.1.2 that this is too simplistic to draw firm conclusions about central accretion structure. The transition radius in Fig. 7 is defined as the radius where the projected accreted density exceeds the projected in-situ density, so replacing the spherical exponential with a realistic thin exponential disc changes the projected in-situ density at 15 kpc by a factor of order 2-3 for plausible scale lengths, and therefore shifts the crossover radius and the surface density at crossover. The paper does not quantify this sensitivity for the M33-analogue sample. Because the abstract and conclusion quote these numbers as precise predictions for observers, this sensitivity should be quantified, for example by applying a simple disc-projection correction or by varying f_mb, before the headline numbers can be taken at face value.
  2. [Section 3.2; Appendix C; conclusions (i)-(iv)] The single free parameter f_mb = 3 per cent is calibrated against the size-mass relation in the regime around M33 and Milky Way masses (Figs. 3 and C1), and it directly sets the in-situ scale length. The conclusions about low-mass halos, in particular the claim that accreted fractions are below 10 per cent and that the accreted and in-situ profiles have similar shapes at M200 < 10^11 Msun, rely on extrapolating this calibration to lower masses. Fig. 3 shows that the predicted size relation flattens near the force-softening scale, and the text cautions that galaxies below M* ~ 10^7 Msun may have artificially large sizes. A demonstration that these conclusions are stable to a small grid of f_mb values, or a comparison against resolved dwarf galaxies with reliable sizes in this regime, would substantially strengthen the low-mass portion of the paper's central claim.
  3. [Sections 3.1 and 4.1; Appendix B1; Figs. 7 and 8] The 'failed Milky Way' population is an acknowledged peculiarity of the L16 galform model that is not present to the same degree in TNG, Eagle, or Auriga, and the paper shows that this population has a significant effect on stellar-mass-selected samples. Since the M33-analogue sample in Figs. 7 and 8 is selected by stellar mass, it includes these failed Milky Ways at high virial mass, and the transition-radius statistics and the conclusion (v) are therefore affected by objects whose existence is disputed. The paper does not show how the transition-radius distribution or the quoted density range changes when failed Milky Ways are removed or when the sample is reweighted to an empirical SMHM relation. Given that the existence of this population is one of the paper's more model-dependent results, this is a relevant robustness test for the quantitative claims.
minor comments (5)
  1. [Section 2.1] The phrase 'Himass function' should read 'HI mass function'.
  2. [Section 2, first paragraph] 'halo with viral mass' should read 'halo with virial mass'.
  3. [Section 3.2] The sentence referring to 'the much steeper locus of S03' is clear, but the earlier reference to 'early types from M15' appears to be a typo; the text in context suggests M13.
  4. [Fig. 3 caption and legend] The legend entry 'Fiducal model (3%)' should be 'Fiducial model (3%)'.
  5. [Abstract and conclusion (v)] The phrase 'the surface density of accreted stars exceeds that of the in situ component at ~15 kpc, corresponding to surface densities 3.5 < log10 ... < 6' is ambiguous because the quoted density range could be read as referring to the accreted component alone rather than the total or the in-situ component at the transition; the text should specify which surface density is being quoted.

Circularity Check

1 steps flagged · score 4.0 of 10

Partial circularity: the f_mb=3% size calibration sets the in-situ profile used for the headline ~15 kpc transition, while the accreted-halo predictions themselves remain independently grounded.

  1. fitted input called prediction [Section 3.2 (f_mb calibration); Section 5.1.1 (transition-radius definition); Section 7 conclusion (v)]
    "As described in Section 2.2, the sizes of low-mass central galaxies in our model are determined almost directly by our choice of f_mb, the free parameter of STINGS... For this reason, we use the observed size–mass relation in the low-mass regime to determine f_mb. ... At this stellar mass scale, the surface density of accreted stars exceeds that of the in situ component at ≈15 kpc, corresponding to surface densities 3.5≲log10 Mstar/Msun kpc−2 ≲6, varying systematically with virial mass."

    Conclusion (v)'s transition radius is defined (Section 5.1.1) as the radius where the accreted surface density is an order of magnitude larger than the in-situ surface density. Section 3.2 states that low-mass galaxy sizes are 'determined almost directly' by f_mb, and that f_mb is chosen to match the observed size–mass relation. The in-situ profile entering the crossover is therefore a calibrated quantity, not a free prediction; the 1% f_mb variant shown in Fig. 13 shifts that profile. The accreted profile is independently computed from orbital dynamics, but the quoted ≈15 kpc is the intersection of that profile with a fitted curve, so the headline number is partly constructed from the calibration rather than derived from first principles.

full rationale

No load-bearing self-citation chain is present: the STINGS method is validated against the independent Auriga hydrodynamical simulations (Appendix D) and compared with Eagle simulations, so the C13/C17 self-citations are not the sole support. No uniqueness theorem is imported, and no external result is renamed as a new prediction. The principal concern is the f_mb parameter: it is openly fitted to the observed size–mass relation, and this calibration directly sets the scale and normalization of the in-situ component used to define the 'transition radius' in conclusion (v). That quantitative claim is therefore partly a recalibration of the fitted input rather than a parameter-free prediction. However, the core accreted-halo results—accreted mass fractions, density profiles, scale lengths, and the comparison to M33 stellar-halo data—are not fitted to those same data; they emerge from the merger trees and orbital dynamics of the COCO simulation. The paper also explicitly identifies the spherical-exponential shape of the in-situ component as a limitation rather than a discovery. The circularity is real but partial and does not invalidate the independent accreted-halo content.

Assumptions & free parameters 1 free parameters · 7 assumptions · 0 invented entities

The central predictions rest on the particle tagging ansatz (universal f_mb, no baryonic back-reaction) and on the L16 GALFORM model; the paper is transparent about both. No new physical entities are introduced.

free parameters (1)
  • f_mb (most-bound fraction) = 3% fiducial; 1% variant
    Fraction of dark matter particles, ranked by binding energy, tagged to represent each new stellar population. Calibrated to the observed size-mass relation for low-mass galaxies (Section 3.2). Directly sets in-situ scale lengths and affects all profile amplitudes.
assumptions (7)
  • domain assumption Lambda CDM cosmology with WMAP7 parameters (Omega_m=0.272, h=0.704, sigma8=0.81) is the correct framework for galaxy formation.
    Used throughout; COCO is run in this cosmology (Section 2).
  • domain assumption Dark-matter-only N-body simulation with particle mass 1.6e5 Msun and softening 0.327 kpc resolves all star-forming progenitor halos down to about 2e8 Msun.
    The paper claims COCO can resolve a 2e8 Msun halo with 1000 particles; the reliability of low-mass predictions depends on this (Sections 2, 3.2).
  • domain assumption GALFORM L16 semi-analytic model, with its calibrated feedback and star-formation prescriptions, yields correct stellar mass growth histories for low-mass galaxies.
    The tagging is run on L16 merger trees; Section 2.1 and Appendix B1 discuss model dependence.
  • ad hoc to paper STINGS tagging assumption: stars in each new stellar population follow the phase-space distribution of the most-bound fraction f_mb of dark matter particles in their host halo, and baryons do not alter the potential.
    Section 2.3.3; this is the core approximation and is not derived from first principles.
  • domain assumption SUBFIND and DHALO merger trees correctly track satellite disruption and accretion events, and disabling orphan galaxies is acceptable at this resolution.
    Section 2 and Appendix B2; if satellites disrupt too early or too late, accreted mass profiles change.
  • ad hoc to paper In-situ stars are distributed as a spherical exponential set by the f_mb threshold, rather than a thin disc.
    Explicit in Section 5.1.2; the paper notes this is too simplistic for bulge conclusions.
  • ad hoc to paper The M33 observational comparison uses assumed RR Lyrae specific frequency S_RR=50-100 and mass-to-light ratio M/L~3 to convert counts to mass.
    Section 5.3; the paper labels this a very rough estimate and says the amplitude is highly uncertain.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Simulations of the accreted stellar halos of low-mass field galaxies." pith.science (2026). https://pith.science/paper/AXJUF23L

@misc{pith2026250113317,
  author       = {Pith},
  title        = {Pith review of: Simulations of the accreted stellar halos of low-mass field galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AXJUF23L}},
  note         = {Machine review of arXiv:2501.13317}
}
abstract

We predict the properties of stellar halos in galaxies of present-day virial mass $10^8 < M_{200} < 10^{12} {\rm M_\odot}$ by combining the GALFORM semi-analytic model of galaxy formation, the COCO cosmological N-body simulation, and the STINGS particle tagging technique. Galaxies in low mass halos have a wide range of stellar halo properties. Their diversity is much greater at fixed stellar mass than fixed virial mass. In the least massive DM halos capable of supporting galaxy formation, accreted mass fractions are < 10 per cent, and the typical density profile of accreted stars is similar to that of stars formed in situ. In low mass galaxies, the radial scale lengths of accreted stellar halos are smaller than those of stellar disks formed in situ, but the accreted component is still diffuse, not bulge-like. At the scale of galaxies like M33, the surface density of accreted stars exceeds that of the in situ component at ~15kpc; the accreted surface density at this radius is $3.5\lesssim \log_{10} M_\star/{\rm M_\odot} \mathrm{kpc^{-2}} \lesssim 6$, varying systematically with virial mass. We compare our predictions to observations and other cosmological simulations. A small but significant number of $\sim 10^{12} {\rm M_\odot}$ halos with exceptionally inefficient star formation -- "failed Milky Ways" -- are more prominent in our model than others; the true abundance of this population is a potential constraint on galaxy formation physics and could be measured by low surface brightness surveys targeting field galaxies with M33-like stellar mass. The stellar particle data for our simulation is publicly available.

Figures

Figures reproduced from arXiv: 2501.13317 by the authors.

Figure 1
Figure 1. Images of logarithmic stellar mass surface density for 9 of the 14 galaxies in our fiducial coco simulation having stellar mass similar to the MW. Each panel shows a 400 × 400 kpc region. The logarithmic range of surface density is 0 < log10 Σ/ M⊙ kpc−2 < 9. 2.3 Fiducial particle tagging model In this paper we examine a fiducial coco-L16 stings model. Our tagging method is controlled by a single free parameter, 𝑓mb,… view at source ↗
Figure 2
Figure 2. Left panel: The relation between stellar mass and virial mass for central galaxies in our fiducial model (black contours/points; see footnote 8 in the text) and Illustris TNG-100 (grey contours/points). For masses below the break at 1012 M⊙, thick purple dashed lines trace the median of our fiducial model ±2 RMS (beyond the break, thinner lines trace the same locus in the model of C13). Green, purple, and yellow ban… view at source ↗
Figure 3
Figure 3. Relation between stellar mass, 𝑀★, and projected stellar half-mass radius, 𝑅50, for central galaxies in our fiducial model (black contours, grey points in both panels; contours are drawn as in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: Fraction of stellar mass accreted by galaxies in our fiducial model as a function of stellar mass. The dashed grey line indicates the lower stellar mass limit of the analysis in C13. To the right of this line, points from our fiducial model are larger and outlined, to …
Figure 5
Figure 5. Figure 5: Fraction of stellar mass accreted by galaxies in our fiducial model as a function of virial mass. We mark the approximate location of the Milky Way and the envelope of Eagle/C-Eagle galaxies from Proctor et al. (2024b) with an offset in 𝑀acc/𝑀★ of +0.2 dex as in [PITH…
Figure 6
Figure 6. Figure 6: Relations between virial mass and stellar mass formed in situ (or￾ange contours and points) or accreted (blue contours and points). Individual points are used where the density is too low to draw meaningful contours. The black hexagons show the total stellar mass for a…
Figure 7
Figure 7. Figure 7: Stellar mass surface density profiles of M33 analogues defined by stellar mass (see the text and [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: These ‘heatmaps’ show five statistics of the ‘observable’ stellar haloes around galaxies in our simulations, averaged in bins of stellar mass and virial mass. From left to right respectively, panels show the total mass of accreted stars beyond the transition radius; th…
Figure 9
Figure 9. Figure 9: Decomposition of average projected stellar mass surface density profiles for low mass galaxies in our model, in bins of virial mass. Fainter, thicker lines show the median density of in situ stars (orange) and accreted stars (blue). Darker lines of the same colour show…
Figure 10
Figure 10. Figure 10: The parameters of single-Sersic profile fits to the in situ stars (orange points) and accreted stars (blue points) in our simulation. From top to bottom, panels plot virial mass against Sersic index, 𝑛; half-mass radius, 𝑅50; and central surface density, Σ0. Lines sho…
Figure 11
Figure 11. Figure 11: Stellar mass surface density profiles of M33 analogues (defined by stellar mass) in bins of virial mass, identical to [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: As [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Decomposition of average projected stellar mass surface density profiles for low mass galaxies in our model, as [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: The fraction of galaxies of a given stellar mass in which more than 90 per cent (black line) or more than 50 per cent (red line) of the accreted mass is contributed by the most massive progenitor. These lines only count galaxies with progenitors, and only stellar mass…
Figure 15
Figure 15. Figure 15: The distribution of 𝑁sig, a measure of the number of significant progenitors of the accreted stellar halo (defined in the text), for galaxies in three ranges of total stellar mass. The vertical scale shows the fraction of galaxies from each mass range in a given bin (…
Figure 16
Figure 16. Figure 16: Projected age profiles in bins of virial mass (log10 𝑀200/ M⊙, shown in each panel along with the number of galaxies in each bin). Lines show the median total (black), accreted (blue) and in situ (orange) age at a given radius; blue and orange ranges show the 16-84th …
Figure 17
Figure 17. Figure 17: Age profiles in bins of galaxy stellar mass. Green arrows mark the inflection point of 𝑔 − 𝑟 with radius found for galaxies stacked in similar ranges of stellar mass by Wang et al. (2019). The number next to each arrow gives the approximate mean log10 𝑀★/ M⊙ of the st…
Figure 18
Figure 18. Figure 18: Age profiles for individual galaxies selected in a range of stellar mass 3 × 109 < 𝑀★ < 7 × 109 M⊙. For each galaxy, log10 𝑀200/M⊙ is given in the upper right of the panel. Letters are labels referred to in the text. The shaded orange and blue regions indicate the ran…
Figure 19
Figure 19. Figure 19: Metallicity profiles in bins of virial mass. Metallicity, [M/H], is defined as the ratio of metal mass to total mass, as defined in the text. Colours and labels are as in [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]
Figure 20
Figure 20. Figure 20: Metallicity profiles in bins of stellar mass. Colours and labels are as in [PITH_FULL_IMAGE:figures/full_fig_p023_20.png]
Figure 21
Figure 21. Figure 21: Relation between progenitor mass and mean metallicity of accreted stars (blue) and all stars (black) measured at three different radii (panel labels). Green contours and points show the overall mass–metallicity relation for progenitor galaxies. Purple bands indicate t…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

164 extracted references · 24 canonical work pages

  1. [1]

    G., Navarro J

    Abadi M. G., Navarro J. F., Steinmetz M., Eke V. R., 2003, @doi [ ] 10.1086/375512 , https://ui.adsabs.harvard.edu/abs/2003ApJ...591..499A 591, 499

    doi:10.1086/375512 2003

  2. [2]

    G., van Dokkum P

    Abraham R. G., van Dokkum P. G., 2014, @doi [ ] 10.1086/674875 , http://adsabs.harvard.edu/abs/2014PASP..126...55A 126, 55

    doi:10.1086/674875 2014

  3. [3]

    C., 2017a, @doi [ ] 10.1093/mnras/stw2229 , http://adsabs.harvard.edu/abs/2017MNRAS.464.2882A 464, 2882

    Amorisco N. C., 2017a, @doi [ ] 10.1093/mnras/stw2229 , http://adsabs.harvard.edu/abs/2017MNRAS.464.2882A 464, 2882

    doi:10.1093/mnras/stw2229

  4. [4]

    C., 2017b, @doi [ ] 10.1093/mnrasl/slx044 , http://adsabs.harvard.edu/abs/2017MNRAS.469L..48A 469, L48

    Amorisco N. C., 2017b, @doi [ ] 10.1093/mnrasl/slx044 , http://adsabs.harvard.edu/abs/2017MNRAS.469L..48A 469, L48

    doi:10.1093/mnrasl/slx044

  5. [5]

    Annibali F., et al., 2020, @doi [ ] 10.1093/mnras/stz3185 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5101A 491, 5101

    doi:10.1093/mnras/stz3185 2020

  6. [6]

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

    doi:10.1051/0004-6361/201322068 2013

  7. [7]

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

    doi:10.3847/1538-3881/aabc4f 2018

  8. [8]

    Astropy Collaboration et al., 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

    doi:10.3847/1538-4357/ac7c74 2022

Show all 164 references
  1. [9]

    F., Valluri M., Stinson G

    Bailin J., Bell E. F., Valluri M., Stinson G. S., Debattista V. P., Couchman H. M. P., Wadsley J., 2014, @doi [ ] 10.1088/0004-637X/783/2/95 , http://adsabs.harvard.edu/abs/2014ApJ...783...95B 783, 95

    2014 doi

  2. [10]

    H., Hearin A

    Behroozi P., Wechsler R. H., Hearin A. P., Conroy C., 2019, @doi [ ] 10.1093/mnras/stz1182 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.3143B 488, 3143

    2019 doi

  3. [11]

    Belokurov V., Kravtsov A., 2023, @doi [ ] 10.1093/mnras/stad2241 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.4456B 525, 4456

    2023 doi

  4. [12]

    W., Koposov S

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

    2018 doi

  5. [13]

    Benitez-Llambay A., Frenk C., 2020, @doi [ ] 10.1093/mnras/staa2698 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.4887B 498, 4887

    2020 doi

  6. [14]

    S., Ludlow A

    Ben \' tez-Llambay A., Frenk C. S., Ludlow A. D., Navarro J. F., 2019, @doi [ ] 10.1093/mnras/stz1890 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2387B 488, 2387

    2019 doi

  7. [17]

    K., Fischer J

    Bernardi M., Meert A., Sheth R. K., Fischer J. L., Huertas-Company M., Maraston C., Shankar F., Vikram V., 2017, @doi [ ] 10.1093/mnras/stx176 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.467.2217B 467, 2217

    2017 doi

  8. [18]

    Boardman N., et al., 2020, @doi [ ] 10.1093/mnras/staa2731 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.4943B 498, 4943

    2020 doi

  9. [19]

    J., 2023, @doi [ ] 10.1093/mnras/stad1123 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5013B 522, 5013

    Bose S., Deason A. J., 2023, @doi [ ] 10.1093/mnras/stad1123 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5013B 522, 5013

    2023 doi

  10. [20]

    Bose S., et al., 2017, @doi [ ] 10.1093/mnras/stw2686 , http://adsabs.harvard.edu/abs/2017MNRAS.464.4520B 464, 4520

    2017 doi

  11. [21]

    J., Frenk C

    Bose S., Deason A. J., Frenk C. S., 2018, @doi [ ] 10.3847/1538-4357/aacbc4 , https://ui.adsabs.harvard.edu/abs/2018ApJ...863..123B 863, 123

    2018 doi

  12. [22]

    G., Benson A

    Bower R. G., Benson A. J., Malbon R., Helly J. C., Frenk C. S., Baugh C. M., Cole S., Lacey C. G., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10519.x , http://adsabs.harvard.edu/abs/2006MNRAS.370..645B 370, 645

    2006

  13. [23]

    G., Schaye J., Frenk C

    Bower R. G., Schaye J., Frenk C. S., Theuns T., Schaller M., Crain R. A., McAlpine S., 2017, @doi [ ] 10.1093/mnras/stw2735 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465...32B 465, 32

    2017 doi

  14. [27]

    S., Johnston K

    Bullock J. S., Johnston K. V., 2005, @doi [ ] 10.1086/497422 , http://adsabs.harvard.edu/abs/2005ApJ...635..931B 635, 931

    2005 doi

  15. [28]

    S., Kravtsov A

    Bullock J. S., Kravtsov A. V., Weinberg D. H., 2001, @doi [ ] 10.1086/318681 , http://adsabs.harvard.edu/abs/2001ApJ...548...33B 548, 33

    2001 doi

  16. [29]

    M., et al., 2019, @doi [ ] 10.1093/mnras/stz365 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.5453C 484, 5453

    Callingham T. M., et al., 2019, @doi [ ] 10.1093/mnras/stz365 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.5453C 484, 5453

    2019 doi

  17. [30]

    L., et al., 2016, @doi [ ] 10.3847/2041-8205/828/1/L5 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828L...5C 828, L5

    Carlin J. L., et al., 2016, @doi [ ] 10.3847/2041-8205/828/1/L5 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828L...5C 828, L5

    2016 doi

  18. [31]

    Chandro-G \'o mez \'A ., et al., 2025, @doi [ ] 10.1093/mnras/staf519 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.539..776C 539, 776

    2025 doi

  19. [32]

    Chiti A., et al., 2021, @doi [Nature Astronomy] 10.1038/s41550-020-01285-w , https://ui.adsabs.harvard.edu/abs/2021NatAs...5..392C 5, 392

    2021 doi

  20. [33]

    Cockcroft R., et al., 2013, @doi [ ] 10.1093/mnras/sts112 , http://adsabs.harvard.edu/abs/2013MNRAS.428.1248C 428, 1248

    2013 doi

  21. [34]

    G., Baugh C

    Cole S., Lacey C. G., Baugh C. M., Frenk C. S., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03879.x , http://adsabs.harvard.edu/abs/2000MNRAS.319..168C 319, 168

    2000

  22. [35]

    Conroy C., et al., 2024, @doi [ ] 10.3847/1538-4357/ad414a , https://ui.adsabs.harvard.edu/abs/2024ApJ...968..129C 968, 129

    2024 doi

  23. [37]

    P., D'Souza R., Kauffmann G., Wang J., Boylan-Kolchin M., Guo Q., Frenk C

    Cooper A. P., D'Souza R., Kauffmann G., Wang J., Boylan-Kolchin M., Guo Q., Frenk C. S., White S. D. M., 2013, @doi [ ] 10.1093/mnras/stt1245 , http://adsabs.harvard.edu/abs/2013MNRAS.434.3348C 434, 3348

    2013 doi

  24. [38]

    P., Gao L., Guo Q., Frenk C

    Cooper A. P., Gao L., Guo Q., Frenk C. S., Jenkins A., Springel V., White S. D. M., 2015a, @doi [ ] 10.1093/mnras/stv1042 , http://ui.adsabs.harvard.edu/abs/2015MNRAS.451.2703C 451, 2703

    doi

  25. [39]

    P., Parry O

    Cooper A. P., Parry O. H., Lowing B., Cole S., Frenk C., 2015b, @doi [ ] 10.1093/mnras/stv2057 , http://adsabs.harvard.edu/abs/2015MNRAS.454.3185C 454, 3185

    doi

  26. [40]

    P., Cole S., Frenk C

    Cooper A. P., Cole S., Frenk C. S., Le Bret T., Pontzen A., 2017, @doi [ ] 10.1093/mnras/stx955 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469.1691C 469, 1691

    2017 doi

  27. [41]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    Crain R. A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    2015 doi

  28. [42]

    F., 2018, @doi [ ] 10.1093/mnras/stx3081 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.5300D 474, 5300

    D'Souza R., Bell E. F., 2018, @doi [ ] 10.1093/mnras/stx3081 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.5300D 474, 5300

    2018 doi

  29. [43]

    D'Souza R., Kauffman G., Wang J., Vegetti S., 2014, @doi [ ] 10.1093/mnras/stu1194 , http://adsabs.harvard.edu/abs/2014MNRAS.443.1433D 443, 1433

    2014 doi

  30. [44]

    D'Souza R., Vegetti S., Kauffmann G., 2015, @doi [ ] 10.1093/mnras/stv2234 , http://adsabs.harvard.edu/abs/2015MNRAS.454.4027D 454, 4027

    2015 doi

  31. [45]

    S., White S

    Davis M., Efstathiou G., Frenk C. S., White S. D. M., 1985, @doi [ ] 10.1086/163168 , https://ui.adsabs.harvard.edu/abs/1985ApJ...292..371D 292, 371

    1985 doi

  32. [46]

    J., Belokurov V., Evans N

    Deason A. J., Belokurov V., Evans N. W., Johnston K. V., 2013, @doi [ ] 10.1088/0004-637X/763/2/113 , http://adsabs.harvard.edu/abs/2013ApJ...763..113D 763, 113

    2013 doi

  33. [47]

    J., et al., 2021, @doi [ ] 10.1093/mnras/staa3984 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.5964D 501, 5964

    Deason A. J., et al., 2021, @doi [ ] 10.1093/mnras/staa3984 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.5964D 501, 5964

    2021 doi

  34. [48]

    J., Bose S., Fattahi A., Amorisco N

    Deason A. J., Bose S., Fattahi A., Amorisco N. C., Hellwing W., Frenk C. S., 2022, @doi [ ] 10.1093/mnras/stab3524 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.4044D 511, 4044

    2022 doi

  35. [50]

    A., van den Bosch F

    Dutton A. A., van den Bosch F. C., Dekel A., Courteau S., 2007, @doi [ ] 10.1086/509314 , http://adsabs.harvard.edu/abs/2007ApJ...654...27D 654, 27

    2007 doi

  36. [51]

    M., Sales L

    Elias L. M., Sales L. V., Creasey P., Cooper M. C., Bullock J. S., Rich R. M., Hernquist L., 2018, @doi [ ] 10.1093/mnras/sty1718 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.4004E 479, 4004

    2018 doi

  37. [52]

    Errani R., Pe \ n arrubia J., Laporte C. F. P., G \'o mez F. A., 2017, @doi [ ] 10.1093/mnrasl/slw211 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465L..59E 465, L59

    2017 doi

  38. [53]

    F., Pe \ n arrubia J., Famaey B., Ibata R., 2023, @doi [ ] 10.1093/mnras/stac3499 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519..384E 519, 384

    Errani R., Navarro J. F., Pe \ n arrubia J., Famaey B., Ibata R., 2023, @doi [ ] 10.1093/mnras/stac3499 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519..384E 519, 384

    2023 doi

  39. [54]

    F., Pe \ n arrubia J., Walker M

    Errani R., Ibata R., Navarro J. F., Pe \ n arrubia J., Walker M. G., 2024, @doi [ ] 10.3847/1538-4357/ad402d , https://ui.adsabs.harvard.edu/abs/2024ApJ...968...89E 968, 89

    2024 doi

  40. [55]

    S., Johnston K

    Font A. S., Johnston K. V., Bullock J. S., Robertson B. E., 2006, @doi [ ] 10.1086/498970 , http://adsabs.harvard.edu/abs/2006ApJ...638..585F 638, 585

    2006 doi

  41. [58]

    S., et al., 2020, @doi [ ] 10.1093/mnras/staa2463 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.1765F 498, 1765

    Font A. S., et al., 2020, @doi [ ] 10.1093/mnras/staa2463 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.1765F 498, 1765

    2020 doi

  42. [59]

    S., White S

    Frenk C. S., White S. D. M., Davis M., Efstathiou G., 1988, @doi [ ] 10.1086/166213 , http://adsabs.harvard.edu/abs/1988ApJ...327..507F 327, 507

    1988 doi

  43. [60]

    M., et al., 2022, @doi [ ] 10.3847/1538-4357/ac3480 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924..116G 924, 116

    Gilbert K. M., et al., 2022, @doi [ ] 10.3847/1538-4357/ac3480 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924..116G 924, 116

    2022 doi

  44. [61]

    Gilhuly C., et al., 2022, @doi [ ] 10.3847/1538-4357/ac6750 , https://ui.adsabs.harvard.edu/abs/2022ApJ...932...44G 932, 44

    2022 doi

  45. [62]

    Goater A., et al., 2024, @doi [ ] 10.1093/mnras/stad3354 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.2403G 527, 2403

    2024 doi

  46. [63]

    A., et al., 2017, @doi [ ] 10.1093/mnras/stx2149 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.3722G 472, 3722

    G \'o mez F. A., et al., 2017, @doi [ ] 10.1093/mnras/stx2149 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.3722G 472, 3722

    2017 doi

  47. [64]

    Gommers R., et al., 2024, scipy/scipy: SciPy 1.14.1, @doi 10.5281/zenodo.13352243 , https://doi.org/10.5281/zenodo.13352243

    2024 doi

  48. [65]

    G., Baugh C

    Gonzalez-Perez V., Lacey C. G., Baugh C. M., Lagos C. D. P., Helly J., Campbell D. J. R., Mitchell P. D., 2014, @doi [ ] 10.1093/mnras/stt2410 , http://adsabs.harvard.edu/abs/2014MNRAS.439..264G 439, 264

    2014 doi

  49. [66]

    Gozman K., et al., 2023, @doi [ ] 10.3847/1538-4357/acbe3a , https://ui.adsabs.harvard.edu/abs/2023ApJ...947...21G 947, 21

    2023 doi

  50. [67]

    Grand R. J. J., et al., 2017, @doi [ ] 10.1093/mnras/stx071 , http://adsabs.harvard.edu/abs/2017MNRAS.467..179G 467, 179

    2017 doi

  51. [68]

    Grand R. J. J., Fragkoudi F., G \'o mez F. A., Jenkins A., Marinacci F., Pakmor R., Springel V., 2024, @doi [ ] 10.1093/mnras/stae1598 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.1814G 532, 1814

    2024 doi

  52. [69]

    B., van den Bosch F

    Green S. B., van den Bosch F. C., Jiang F., 2021, @doi [ ] 10.1093/mnras/stab696 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.4075G 503, 4075

    2021 doi

  53. [70]

    B., van den Bosch F

    Green S. B., van den Bosch F. C., Jiang F., 2022, @doi [ ] 10.1093/mnras/stab3130 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2624G 509, 2624

    2022 doi

  54. [72]

    Guo Q., White S., 2014, @doi [ ] 10.1093/mnras/stt2116 , http://adsabs.harvard.edu/abs/2014MNRAS.437.3228G 437, 3228

    2014 doi

  55. [74]

    Guo Q., et al., 2011a, @doi [ ] 10.1111/j.1365-2966.2010.18114.x , http://adsabs.harvard.edu/abs/2011MNRAS.413..101G 413, 101

    2010

  56. [76]

    Guzman R., et al., 2022, Analysis of Resolved Remnants of Accreted galaxies as a Key Instrument for Halo Surveys, ARRAKIHS Proposal https://www.cosmos.esa.int/documents/7423467/7423486/ESA-F2-ARRAKIHS-Phase-2-PUBLIC-v0.9.2.pdf/61b363d7-2a06-1196-5c40-c85aa90c2113?t=1667557422996

    2022

  57. [77]

    F., de Jong R

    Harmsen B., Monachesi A., Bell E. F., de Jong R. S., Bailin J., Radburn-Smith D. J., Holwerda B. W., 2017, @doi [ ] 10.1093/mnras/stw2992 , http://adsabs.harvard.edu/abs/2017MNRAS.466.1491H 466, 1491

    2017 doi

  58. [78]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    2020 doi

  59. [79]

    A., Frenk C

    Hellwing W. A., Frenk C. S., Cautun M., Bose S., Helly J., Jenkins A., Sawala T., Cytowski M., 2016, @doi [ ] 10.1093/mnras/stw214 , http://adsabs.harvard.edu/abs/2016MNRAS.457.3492H 457, 3492

    2016 doi

  60. [80]

    R., McConnachie A

    Higgs C. R., McConnachie A. W., Annau N., Irwin M., Battaglia G., C \^o t \'e P., Lewis G. F., Venn K., 2021, @doi [ ] 10.1093/mnras/stab002 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503..176H 503, 176

    2021 doi

  61. [81]

    K., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2405.13499 , https://ui.adsabs.harvard.edu/abs/2024arXiv240513499H p

    Hunt L. K., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2405.13499 , https://ui.adsabs.harvard.edu/abs/2024arXiv240513499H p. arXiv:2405.13499

    2024 doi

  62. [82]

    D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    2007 doi

  63. [83]

    Irwin M., Hatzidimitriou D., 1995, @doi [ ] 10.1093/mnras/277.4.1354 , https://ui.adsabs.harvard.edu/abs/1995MNRAS.277.1354I 277, 1354

    1995 doi

  64. [84]

    S., de Jong R

    Jang I. S., de Jong R. S., Holwerda B. W., Monachesi A., Bell E. F., Bailin J., 2020a, @doi [ ] 10.1051/0004-6361/201936994 , https://ui.adsabs.harvard.edu/abs/2020A&A...637A...8J 637, A8

    doi

  65. [85]

    S., et al., 2020b, @doi [ ] 10.1051/0004-6361/202038651 , https://ui.adsabs.harvard.edu/abs/2020A&A...640L..19J 640, L19

    Jang I. S., et al., 2020b, @doi [ ] 10.1051/0004-6361/202038651 , https://ui.adsabs.harvard.edu/abs/2020A&A...640L..19J 640, L19

    doi

  66. [86]

    R., Sestito F., McConnachie A

    Jensen J., Hayes C. R., Sestito F., McConnachie A. W., Waller F., Smith S. E. T., Navarro J., Venn K. A., 2024, @doi [ ] 10.1093/mnras/stad3322 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.4209J 527, 4209

    2024 doi

  67. [87]

    C., Cole S., Frenk C

    Jiang L., Helly J. C., Cole S., Frenk C. S., 2014, @doi [ ] 10.1093/mnras/stu390 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440.2115J 440, 2115

    2014 doi

  68. [88]

    Kado-Fong E., et al., 2022, @doi [ ] 10.3847/1538-4357/ac6c88 , https://ui.adsabs.harvard.edu/abs/2022ApJ...931..152K 931, 152

    2022 doi

  69. [89]

    D., Ricotti M., 2019, @doi [ ] 10.1093/mnras/stz1886 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2673K 488, 2673

    Kang H. D., Ricotti M., 2019, @doi [ ] 10.1093/mnras/stz1886 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2673K 488, 2673

    2019 doi

  70. [90]

    Y., Peter A

    Kim S. Y., Peter A. H. G., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2106.09050 , https://ui.adsabs.harvard.edu/abs/2021arXiv210609050K p. arXiv:2106.09050

    2021 doi arXiv

  71. [91]

    Y., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.15214 , https://ui.adsabs.harvard.edu/abs/2024arXiv240815214K p

    Kim S. Y., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.15214 , https://ui.adsabs.harvard.edu/abs/2024arXiv240815214K p. arXiv:2408.15214

    2024 doi arXiv

  72. [92]

    Kluge M., Bender R., Riffeser A., Goessl C., Hopp U., Schmidt M., Ries C., 2021, @doi [ ] 10.3847/1538-4365/abcda6 , https://ui.adsabs.harvard.edu/abs/2021ApJS..252...27K 252, 27

    2021 doi

  73. [93]

    IOS Press, pp 87 -- 90

    Kluyver T., et al., 2016, in Loizides F., Schmidt B., eds, Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press, pp 87 -- 90

    2016

  74. [94]

    Komatsu E., et al., 2011, @doi [ ] 10.1088/0067-0049/192/2/18 , http://adsabs.harvard.edu/abs/2011ApJS..192...18K 192, 18

    2011 doi

  75. [95]

    G., et al., 2016, @doi [ ] 10.1093/mnras/stw1888 , http://adsabs.harvard.edu/abs/2016MNRAS.462.3854L 462, 3854

    Lacey C. G., et al., 2016, @doi [ ] 10.1093/mnras/stw1888 , http://adsabs.harvard.edu/abs/2016MNRAS.462.3854L 462, 3854

    2016 doi

  76. [96]

    Lagos C. D. P., Lacey C. G., Baugh C. M., Bower R. G., Benson A. J., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19160.x , http://adsabs.harvard.edu/abs/2011MNRAS.416.1566L 416, 1566

    2011

  77. [97]

    R., et al., 2007, @doi [ ] 10.1086/518223 , http://adsabs.harvard.edu/abs/2007ApJ...662..808L 662, 808

    Lauer T. R., et al., 2007, @doi [ ] 10.1086/518223 , http://adsabs.harvard.edu/abs/2007ApJ...662..808L 662, 808

    2007 doi

  78. [98]

    P., Frenk C., Zolotov A., Brooks A

    Le Bret T., Pontzen A., Cooper A. P., Frenk C., Zolotov A., Brooks A. M., Governato F., Parry O. H., 2017, @doi [ ] 10.1093/mnras/stx552 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.3212L 468, 3212

    2017 doi

  79. [99]

    Li Y.-S., White S. D. M., 2008, @doi [ ] 10.1111/j.1365-2966.2007.12748.x , http://adsabs.harvard.edu/abs/2008MNRAS.384.1459L 384, 1459

    2008

  80. [100]

    Li Y.-S., Helmi A., De Lucia G., Stoehr F., 2009, @doi [ ] 10.1111/j.1745-3933.2009.00690.x , http://adsabs.harvard.edu/abs/2009MNRAS.397L..87L 397, L87

    2009

  81. [101]

    P., 2023, @doi [ ] 10.1093/mnras/stac3327 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.3999L 518, 3999

    Liao L.-W., Cooper A. P., 2023, @doi [ ] 10.1093/mnras/stac3327 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.3999L 518, 3999

    2023 doi

  82. [102]

    C., Newman J

    Licquia T. C., Newman J. A., Bershady M. A., 2016, @doi [ ] 10.3847/1538-4357/833/2/220 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..220L 833, 220

    2016 doi

  83. [103]

    R., Cautun M., Frenk C

    Lovell M. R., Cautun M., Frenk C. S., Hellwing W. A., Newton O., 2021, @doi [ ] 10.1093/mnras/stab2452 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.4826L 507, 4826

    2021 doi

  84. [104]

    D., Fall S

    Ludlow A. D., Fall S. M., Wilkinson M. J., Schaye J., Obreschkow D., 2023, @doi [ ] 10.1093/mnras/stad2615 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.5614L 525, 5614

    2023 doi

  85. [106]

    Mart \' nez-Delgado D., et al., 2023, @doi [ ] 10.1051/0004-6361/202245011 , https://ui.adsabs.harvard.edu/abs/2023A&A...671A.141M 671, A141

    2023 doi

  86. [107]

    W., 2012, @doi [ ] 10.1088/0004-6256/144/1/4 , http://adsabs.harvard.edu/abs/2012AJ....144....4M 144, 4

    McConnachie A. W., 2012, @doi [ ] 10.1088/0004-6256/144/1/4 , http://adsabs.harvard.edu/abs/2012AJ....144....4M 144, 4

    2012 doi

  87. [108]

    McMonigal B., et al., 2016a, @doi [ ] 10.1093/mnras/stv2690 , http://adsabs.harvard.edu/abs/2016MNRAS.456..405M 456, 405

    doi

  88. [109]

    McMonigal B., et al., 2016b, @doi [ ] 10.1093/mnras/stw1657 , http://adsabs.harvard.edu/abs/2016MNRAS.461.4374M 461, 4374

    doi

  89. [110]

    Merritt A., van Dokkum P., Abraham R., Zhang J., 2016, @doi [ ] 10.3847/0004-637X/830/2/62 , https://ui.adsabs.harvard.edu/abs/2016ApJ...830...62M 830, 62

    2016 doi

  90. [111]

    Merritt A., Pillepich A., van Dokkum P., Nelson D., Hernquist L., Marinacci F., Vogelsberger M., 2020, @doi [ ] 10.1093/mnras/staa1164 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4570M 495, 4570

    2020 doi

  91. [112]

    arXiv:2409.03585

    Miro-Carretero J., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2409.03585 , https://ui.adsabs.harvard.edu/abs/2024arXiv240903585M p. arXiv:2409.03585

    2024 doi arXiv

  92. [113]

    D., Lacey C

    Mitchell P. D., Lacey C. G., Baugh C. M., Cole S., 2013, @doi [ ] 10.1093/mnras/stt1280 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435...87M 435, 87

    2013 doi

  93. [114]

    J., Mao S., White S

    Mo H. J., Mao S., White S. D. M., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01227.x , https://ui.adsabs.harvard.edu/abs/1998MNRAS.295..319M 295, 319

    1998

  94. [115]

    F., Radburn-Smith D

    Monachesi A., Bell E. F., Radburn-Smith D. J., Bailin J., de Jong R. S., Holwerda B., Streich D., Silverstein G., 2016a, @doi [ ] 10.1093/mnras/stv2987 , http://adsabs.harvard.edu/abs/2016MNRAS.457.1419M 457, 1419

    doi

  95. [116]

    A., Grand R

    Monachesi A., G \'o mez F. A., Grand R. J. J., Kauffmann G., Marinacci F., Pakmor R., Springel V., Frenk C. S., 2016b, @doi [ ] 10.1093/mnrasl/slw052 , http://adsabs.harvard.edu/abs/2016MNRAS.459L..46M 459, L46

    doi

  96. [117]

    Monachesi A., et al., 2019, @doi [ ] 10.1093/mnras/stz538 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.2589M 485, 2589

    2019 doi

  97. [118]

    J., Franx M., 2013, @doi [ ] 10.1088/0004-637X/777/2/117 , http://adsabs.harvard.edu/abs/2013ApJ...777..117M 777, 117

    Mosleh M., Williams R. J., Franx M., 2013, @doi [ ] 10.1088/0004-637X/777/2/117 , http://adsabs.harvard.edu/abs/2013ApJ...777..117M 777, 117

    2013 doi

  98. [119]

    P., Naab T., White S

    Moster B. P., Naab T., White S. D. M., 2018, @doi [ ] 10.1093/mnras/sty655 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1822M 477, 1822

    2018 doi

  99. [120]

    F., Eke V

    Navarro J. F., Eke V. R., Frenk C. S., 1996, @doi [ ] 10.1093/mnras/283.3.72L , http://adsabs.harvard.edu/abs/1996MNRAS.283L..72N 283, L72

    1996 doi

  100. [121]

    Ogami I., et al., 2024, @doi [ ] 10.3847/1538-4357/ad5445 , https://ui.adsabs.harvard.edu/abs/2024ApJ...971..107O 971, 107

    2024 doi

  101. [122]

    Orkney M. D. A., et al., 2021, @doi [ ] 10.1093/mnras/stab1066 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.3509O 504, 3509

    2021 doi

  102. [123]

    T., 2018, @doi [ ] 10.3847/1538-4357/aab78f , http://adsabs.harvard.edu/abs/2018ApJ...857...78P 857, 78

    Patel E., Besla G., Mandel K., Sohn S. T., 2018, @doi [ ] 10.3847/1538-4357/aab78f , http://adsabs.harvard.edu/abs/2018ApJ...857...78P 857, 78

    2018 doi

  103. [124]

    Peebles P. J. E., 2020, @doi [ ] 10.1093/mnras/staa2649 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.4386P 498, 4386

    2020 doi

  104. [125]

    E., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.53 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9c..21P 9, 21

    Perez F., Granger B. E., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.53 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9c..21P 9, 21

    2007 doi

  105. [126]

    Pillepich A., et al., 2014, @doi [ ] 10.1093/mnras/stu1408 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444..237P 444, 237

    2014 doi

  106. [128]

    Posti L., Marasco A., Fraternali F., Famaey B., 2019, @doi [ ] 10.1051/0004-6361/201935982 , https://ui.adsabs.harvard.edu/abs/2019A&A...629A..59P 629, A59

    2019 doi

  107. [129]

    L., Ludlow A

    Proctor K. L., Ludlow A. D., Lagos C. d. P., Robotham A. S. G., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2407.11444 , https://ui.adsabs.harvard.edu/abs/2024arXiv240711444P p. arXiv:2407.11444

    doi arXiv

  108. [130]

    L., Lagos C

    Proctor K. L., Lagos C. d. P., Ludlow A. D., Robotham A. S. G., 2024b, @doi [ ] 10.1093/mnras/stad3341 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.2624P 527, 2624

    doi

  109. [131]

    P., Grand R

    Pu S.-Y., Cooper A. P., Grand R. J. J., G \'o mez F. A., Monachesi A., 2025, @doi [ ] 10.3847/1538-4357/ada382 , https://ui.adsabs.harvard.edu/abs/2025ApJ...980...63P 980, 63

    2025 doi

  110. [132]

    Pucha R., et al., 2019, @doi [ ] 10.3847/1538-4357/ab29fb , https://ui.adsabs.harvard.edu/abs/2019ApJ...880..104P 880, 104

    2019 doi

  111. [133]

    W., Bullock J

    Purcell C. W., Bullock J. S., Zentner A. R., 2007, @doi [ ] 10.1086/519787 , http://adsabs.harvard.edu/abs/2007ApJ...666...20P 666, 20

    2007 doi

  112. [134]

    J., et al., 2011, @doi [ ] 10.1088/0067-0049/195/2/18 , http://adsabs.harvard.edu/abs/2011ApJS..195...18R 195, 18

    Radburn-Smith D. J., et al., 2011, @doi [ ] 10.1088/0067-0049/195/2/18 , http://adsabs.harvard.edu/abs/2011ApJS..195...18R 195, 18

    2011 doi

  113. [135]

    P., Pontzen A., Saintonge A., 2019a, @doi [ ] 10.1093/mnras/stz552 , http://adsabs.harvard.edu/abs/2019MNRAS.485.1906R 485, 1906

    Rey M. P., Pontzen A., Saintonge A., 2019a, @doi [ ] 10.1093/mnras/stz552 , http://adsabs.harvard.edu/abs/2019MNRAS.485.1906R 485, 1906

    1906 doi

  114. [136]

    P., Pontzen A., Agertz O., Orkney M

    Rey M. P., Pontzen A., Agertz O., Orkney M. D. A., Read J. I., Saintonge A., Pedersen C., 2019b, @doi [ ] 10.3847/2041-8213/ab53dd , https://ui.adsabs.harvard.edu/abs/2019ApJ...886L...3R 886, L3

    doi

  115. [137]

    Richings J., et al., 2020, @doi [ ] 10.1093/mnras/stz3448 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5780R 492, 5780

    2020 doi

  116. [138]

    Ricotti M., Polisensky E., Cleland E., 2022, @doi [ ] 10.1093/mnras/stac1485 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515..302R 515, 302

    2022 doi

  117. [139]

    Ristea A., Cortese L., Groves B., Fraser-McKelvie A., Obreschkow D., Glazebrook K., 2024, @doi [ ] 10.1093/mnras/stae2085 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534..995R 534, 995

    2024 doi

  118. [140]

    Santos-Santos I. M. E., Frenk C. S., Navarro J. F., Cole S., Helly J., 2025, @doi [ ] 10.1093/mnras/staf749 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.tmp..721S

    2025 doi

  119. [141]

    Scannapieco C., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20993.x , http://adsabs.harvard.edu/abs/2012MNRAS.423.1726S 423, 1726

    2012

  120. [142]

    J., White S

    Shen S., Mo H. J., White S. D. M., Blanton M. R., Kauffmann G., Voges W., Brinkmann J., Csabai I., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06740.x , http://adsabs.harvard.edu/abs/2003MNRAS.343..978S 343, 978

    2003

  121. [143]

    arXiv:2410.09143

    Shipp N., et al., 2024, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2024arXiv241009143S p. arXiv:2410.09143

    2024 arXiv

  122. [144]

    Simha V., Cole S., 2017, @doi [ ] 10.1093/mnras/stx1942 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.1392S 472, 1392

    2017 doi

  123. [145]

    F., Price P

    Smercina A., Bell E. F., Price P. A., D'Souza R., Slater C. T., Bailin J., Monachesi A., Nidever D., 2018, @doi [ ] 10.3847/1538-4357/aad2d6 , http://adsabs.harvard.edu/abs/2018ApJ...863..152S 863, 152

    2018 doi

  124. [146]

    Smercina A., et al., 2020, @doi [ ] 10.3847/1538-4357/abc485 , https://ui.adsabs.harvard.edu/abs/2020ApJ...905...60S 905, 60

    2020 doi

  125. [147]

    Smercina A., et al., 2023, @doi [ ] 10.3847/2041-8213/acd5d1 , https://ui.adsabs.harvard.edu/abs/2023ApJ...949L..37S 949, L37

    2023 doi

  126. [148]

    Springel V., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09655.x , http://adsabs.harvard.edu/abs/2005MNRAS.364.1105S 364, 1105

    2005

  127. [149]

    Springel V., White S. D. M., Tormen G., Kauffmann G., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04912.x , http://adsabs.harvard.edu/abs/2001MNRAS.328..726S 328, 726

    2001

  128. [150]

    Springel V., et al., 2005, @doi [ ] 10.1038/nature03597 , https://ui.adsabs.harvard.edu/abs/2005Natur.435..629S 435, 629

    2005 doi

  129. [151]

    P., Okamoto S., Spitler L., 2018, @doi [ ] 10.3847/1538-4357/aad9fe , http://adsabs.harvard.edu/abs/2018ApJ...865..125T 865, 125

    Tanaka M., Chiba M., Hayashi K., Komiyama Y., Okamoto T., Cooper A. P., Okamoto S., Spitler L., 2018, @doi [ ] 10.3847/1538-4357/aad9fe , http://adsabs.harvard.edu/abs/2018ApJ...865..125T 865, 125

    2018 doi

  130. [152]

    Tanakul N., Yang S.-C., Sarajedini A., 2017, @doi [ ] 10.1093/mnras/stx515 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468..870T 468, 870

    2017 doi

  131. [153]

    A., et al., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2412.13807 , https://ui.adsabs.harvard.edu/abs/2024arXiv241213807T p

    Tau E. A., et al., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2412.13807 , https://ui.adsabs.harvard.edu/abs/2024arXiv241213807T p. arXiv:2412.13807

    doi arXiv

  132. [154]

    A., Vivas A

    Tau E. A., Vivas A. K., Mart \' nez-V \'a zquez C. E., 2024b, @doi [ ] 10.3847/1538-3881/ad1509 , https://ui.adsabs.harvard.edu/abs/2024AJ....167...57T 167, 57

    doi

  133. [155]

    D., 2002--, PyTables : Hierarchical Datasets in Python , http://www.pytables.org/

    Team P. D., 2002--, PyTables : Hierarchical Datasets in Python , http://www.pytables.org/

    2002

  134. [156]

    Trujillo I., et al., 2021, @doi [ ] 10.1051/0004-6361/202141603 , https://ui.adsabs.harvard.edu/abs/2021A&A...654A..40T 654, A40

    2021 doi

  135. [157]

    L., 2009, Python 3 Reference Manual

    Van Rossum G., Drake F. L., 2009, Python 3 Reference Manual. CreateSpace, Scotts Valley, CA

    2009

  136. [158]

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

    2020 doi

  137. [159]

    S., 2024, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2024arXiv240604405W p

    Wagg T., Broekgaarden F. S., 2024, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2024arXiv240604405W p. arXiv:2406.04405

    2024

  138. [160]

    Wagg T., Broekgaarden F., G \"u ltekin K., 2024, TomWagg/software-citation-station: v1.2, @doi 10.5281/zenodo.13225824 , https://doi.org/10.5281/zenodo.13225824

    2024 doi

  139. [161]

    Wang W., et al., 2019, @doi [ ] 10.1093/mnras/stz1339 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.1580W 487, 1580

    2019 doi

  140. [162]

    N., 2020, @doi [Science China Physics, Mechanics, and Astronomy] 10.1007/s11433-019-1541-6 , https://ui.adsabs.harvard.edu/abs/2020SCPMA..63j9801W 63, 109801

    Wang W., Han J., Cautun M., Li Z., Ishigaki M. N., 2020, @doi [Science China Physics, Mechanics, and Astronomy] 10.1007/s11433-019-1541-6 , https://ui.adsabs.harvard.edu/abs/2020SCPMA..63j9801W 63, 109801

    2020 doi

  141. [163]

    P., Bose S., Frenk C

    Wang C.-W., Cooper A. P., Bose S., Frenk C. S., Hellwing W. A., 2023, @doi [ ] 10.3847/1538-4357/ad011d , https://ui.adsabs.harvard.edu/abs/2023ApJ...958..166W 958, 166

    2023 doi

  142. [164]

    White S. D. M., Frenk C. S., 1991, @doi [ ] 10.1086/170483 , http://adsabs.harvard.edu/abs/1991ApJ...379...52W 379, 52

    1991 doi

  143. [165]

    F., et al., 2021, @doi [ ] 10.3847/1538-4365/abdf4e , https://ui.adsabs.harvard.edu/abs/2021ApJS..253...53W 253, 53

    Williams B. F., et al., 2021, @doi [ ] 10.3847/1538-4365/abdf4e , https://ui.adsabs.harvard.edu/abs/2021ApJS..253...53W 253, 53

    2021 doi

  144. [166]

    Zaritsky D., Behroozi P., 2023, @doi [ ] 10.1093/mnras/stac3610 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519..871Z 519, 871

    2023 doi

  145. [167]

    Zaritsky D., et al., 2024, @doi [ ] 10.3847/1538-3881/ad543f , https://ui.adsabs.harvard.edu/abs/2024AJ....168...69Z 168, 69

    2024 doi

  146. [168]

    S., 2021, @doi [ ] 10.1093/mnras/stab2642 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.2098Z 508, 2098

    Zhang S., Mackey D., Da Costa G. S., 2021, @doi [ ] 10.1093/mnras/stab2642 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.2098Z 508, 2098

    2021 doi

  147. [169]

    Zhu Q., Marinacci F., Maji M., Li Y., Springel V., Hernquist L., 2016, @doi [ ] 10.1093/mnras/stw374 , http://adsabs.harvard.edu/abs/2016MNRAS.458.1559Z 458, 1559

    2016 doi

  148. [170]

    M., Governato F., Brook C

    Zolotov A., Willman B., Brooks A. M., Governato F., Brook C. B., Hogg D. W., Quinn T., Stinson G., 2009, @doi [ ] 10.1088/0004-637X/702/2/1058 , http://adsabs.harvard.edu/abs/2009ApJ...702.1058Z 702, 1058

    2009 doi

  149. [171]

    G., Abraham R., Merritt A., 2014, @doi [ ] 10.1088/2041-8205/782/2/L24 , http://adsabs.harvard.edu/abs/2014ApJ...782L..24V 782, L24

    van Dokkum P. G., Abraham R., Merritt A., 2014, @doi [ ] 10.1088/2041-8205/782/2/L24 , http://adsabs.harvard.edu/abs/2014ApJ...782L..24V 782, L24

    2014 doi

  150. [172]

    C., Ogiya G., 2018, @doi [ ] 10.1093/mnras/sty084 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.4066V 475, 4066

    van den Bosch F. C., Ogiya G., 2018, @doi [ ] 10.1093/mnras/sty084 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.4066V 475, 4066

    2018 doi

  151. [173]

    C., Ogiya G., Hahn O., Burkert A., 2018, @doi [ ] 10.1093/mnras/stx2956 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3043V 474, 3043

    van den Bosch F. C., Ogiya G., Hahn O., Burkert A., 2018, @doi [ ] 10.1093/mnras/stx2956 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3043V 474, 3043

    2018 doi

  152. [174]

    P., Fardal M

    van der Marel R. P., Fardal M. A., Sohn S. T., Patel E., Besla G., del Pino A., Sahlmann J., Watkins L. L., 2019, @doi [ ] 10.3847/1538-4357/ab001b , https://ui.adsabs.harvard.edu/abs/2019ApJ...872...24V 872, 24

    2019 doi

  153. [175]

    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...

  154. [176]

    @esa (Ref

    \@ifclassloaded aguplus natbib The aguplus class already includes natbib coding, so you should not add it explicitly Type <Return> for now, but then later remove the command natbib from the document \@ifclassloaded nlinproc natbib The nlinproc class already includes natbib cod...

  155. [177]

    @stdbsttrue NAT@ctr \@lbibitem[ NAT@ctr ] \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 [ @natanchorstart #2\@extra@b@citeb \@biblabel @num @natanchorend] @ifc...

  156. [178]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifundefined NAT@sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifundefined bib@heading @heading NAT@ctr thebibliography [1] @ \@biblabel NAT@ctr \@bib...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.