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Watching our Galaxy Grow Up: The Mass and Color Evolution of the Milky Way

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read This paper derives the Milky Way's integrated stellar mass and optical colors from measured densities of stellar populations of all ages, runs the clock backward to earlier redshifts, and argues from simulated analogs that the Galaxy…

desk verdict Genuinely new empirical MW mass-color track and interesting TNG50 comparison, but the early-assembly claim needs a significance test and robustness analysis. read the letter →

arxiv 2507.17637 v1 pith:SYJIIVF7 submitted 2025-07-23 astro-ph.GA

classification astro-ph.GA
keywords MilkyWayGalacticarchaeologystellarmassassemblyintegratedcolorsmono-ageabundancepopulationsgreenvalleygalaxyanalogsmass-colorrelation
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 reconstructs the Milky Way as a galaxy: its total stellar mass, its integrated optical colors, and how those quantities have changed over the past 13 billion years. The reconstruction sums the measured densities of stellar populations binned by age, metallicity, and alpha abundance, then 'de-ages' those populations to see what the Galaxy would have looked like at earlier redshifts. The result places the Milky Way in the green valley of the galaxy mass-color plane and, when compared with simulated galaxies matched in mass and color, indicates that the Milky Way assembled a larger share of its stars before z~1-1.5 than typical galaxies of its present-day mass and has produced fewer stars since. A reader should care because this gives an empirical, time-resolved picture of how our own galaxy fits into the broader population, instead of relying on distant galaxies as stand-ins.

What carries the argument

The central object is the MAAP: a mono-age and -abundance population, whose spatial density profile is parameterized as a broken exponential in radius and a flaring exponential in height, fitted to a large spectroscopic sample with an explicit selection-function model. The machinery combines these profiles with stellar isochrones and an assumed initial mass function to convert star counts to masses, extends them to ages below 1 Gyr using a star-formation-rate density profile, replaces unstable inner-Galaxy fits with a capped value at 2.5 kpc, smooths unfitted regions in the age-metallicity plane, and splits each coarse age bin into 0.1 Gyr fine bins. 'Running the clock backward' subtracts a lookback time from each MAAP, discards populations not yet formed, recomputes single-burst magnitudes at the new age and fixed metallicity, and sums the survivors to produce integrated masses and colors at any redshift. The same track then defines mass-color analogs in a cosmological simulation, whose merger trees are followed forward and backward to compare assembly histories.

What would settle it

Measure the stellar mass within R~2.5 kpc of the Galactic center with an independent, selection-function-corrected dataset (for example, deep near-infrared photometry with Gaia parallaxes) and compare it to the mass implied by the capped MAAP profiles; a discrepancy larger than the adopted ~30% uncertainty would shift the integrated mass, colors, and the inferred early-assembly fraction.

Watch

Extended reading notes

Core claim

The paper's central claim is that the Milky Way is an early assembler: by z~1-1.5 it already contained a larger fraction of its present-day stellar mass than do comparable-mass galaxies today, and its star formation since then has been relatively subdued. The evidence comes from integrating ~3300 fine-binned mono-age and -abundance population profiles to get present-day mass and color, then subtracting lookback age from each population to produce a complete mass-color track. Matching that track in a cosmological simulation shows that galaxies selected as analogs of the present-day Milky Way follow a similar evolutionary path, only delayed in time, while galaxies selected as analogs of the early Milky Way scatter into many different outcomes, with a large fraction becoming smaller and redder than the real Galaxy by z=0. The paper thus argues that the Milky Way's early appearance did not determine its later fate.

Load-bearing premise

The corrected MAAP density profiles—including the zeroed, smoothed, and inner-Galaxy-capped values—correctly represent the intrinsic stellar mass distribution of the Milky Way across all ages, metallicities, and alpha abundances.

Editorial extensions

If this is right

  • The Milky Way has likely been in the green valley for only about 1–2 Gyr, so its current transitional color is a recent development rather than a long-term state.
  • Present-day analogs in the simulation had assembled much less of their stellar mass by z~1, implying the real Galaxy is early relative to its mass cohort.
  • Galaxies that look like the Milky Way at z=1–1.5 do not usually end up as Milky Way-like today; 41% of the z=1.5 analogs are already smaller and redder than the present-day Milky Way.
  • The stellar populations of the two largest past merger remnants contribute less than 5 millimagnitudes to the integrated color evolution, so merger debris has no detectable effect on the mass-color track.
  • Mass- and light-weighted metallicity distributions and star formation histories can now be extracted for arbitrary regions of the Galaxy, enabling direct comparisons with extragalactic surveys.

Reading between the lines

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

  • If the early-assembly claim survives more direct probes, the Milky Way becomes a nearby laboratory for testing inside-out quenching and the mechanisms that move galaxies through the green valley without morphological disruption.
  • The de-aging procedure could be applied to future wide-area surveys with better inner-Galaxy coverage to test the capping correction directly and refine the assembly fraction.
  • Because integrated color is insensitive to the merger debris (below 5 mmag), light alone cannot reveal a Milky-Way-like merger history; chemical tagging and kinematics remain the discriminating tools.
  • The mass-color matching used to define analogs could be extended to include star formation rate or environment to test whether the early-MW analogs that stay Milky Way-like are distinguished by some other property at z>1.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper presents a new empirical method for inferring the present-day and time-resolved integrated stellar mass and colors of the Milky Way from mono-age and -abundance population (MAAP) density profiles based on APOGEE DR17 data. The authors combine these MAAPs with PARSEC isochrones and a Kroupa IMF to compute masses and integrated magnitudes, apply ad hoc gap-filling and smoothing to the ~48% of MAAP bins without converged fits, and "de-age" the populations to reconstruct the MW's past mass and color evolution. They compare this empirical track to TNG50 subhaloes selected as mass-color analogs at several redshifts, and from this comparison claim strong evidence that the MW assembled a larger fraction of its stellar mass prior to z~1-1.5 than typical present-day analogs, that present-day analogs follow a similar growth history at slightly later times, and that analogs of the early MW are not guaranteed to evolve into MW-like galaxies today.

Significance. If the central result holds, this would be a valuable demonstration of using resolved stellar populations within the MW to place the Galaxy on extragalactic scaling relations and evolutionary tracks, complementing previous analog-based approaches. The paper's strengths include the explicit use of selection-function-corrected APOGEE data, the careful treatment of stellar evolution and remnant masses, the cross-checks of present-day mass and colors against Licquia & Newman (2015) and Fielder et al. (2021), the MDF comparisons to Rojas-Arriagada et al. (2020) and others, and the use of a large independent cosmological simulation (TNG50) for the analog comparison. However, the central early-assembly claim rests on several ad hoc steps in the MAAP reconstruction, and the "strong evidence" language in the abstract is not backed by a quantitative significance test. These issues are substantial enough that the result, while plausible, is not yet established to the standard implied by the abstract.

major comments (3)
  1. [§6.2, Fig. 10] The claim of "strong evidence" for earlier-than-average stellar mass assembly is not supported by any quantitative significance test. Figure 10 shows the MW's cumulative M*(z)/M*(0) lying above the median TNG50 analog tracks, but the shaded regions show only the absolute median deviation of the analog population, not the uncertainty on the MW track itself. The paper does not report the distribution of analog cumulative fractions at z=1 or z=1.5, nor the probability that a random analog would lie at or above the MW's value. Given that the MW track is itself uncertain (with ~30% mass uncertainties per MAAP and additional systematic choices described in §2.3), the abstract's "strong evidence" is not quantitatively justified. A Monte Carlo propagation of the MAAP mass uncertainties into the cumulative fraction, and a comparison with the analog distribution at fixed redshift, should be provided.
  2. [§2.3.2 and §2.3.3] The central early-assembly claim depends critically on the ad hoc treatment of the ~48% of MAAP bins without converged fits. Section 2.3.3 zeroes unfitted regions by visual inspection and then smooths log-masses in the age-metallicity plane with Gaussian kernels of different widths for low- and high-alpha populations, while §2.3.2 caps the inner-Galaxy density at R=2.5 kpc only for the [M/H]=+0.45, tau>2 Gyr bin. These choices directly set the mass in the old, metal-rich and old, metal-poor populations that dominate the z~1 mass budget. From Table 2, the z=1 cumulative fraction is approximately 10.19/10.59 ~ 0.40; plausible alternative reconstructions (e.g., capping all supersolar bins, using nearest-neighbor or Gaussian-process interpolation instead of zeroing+smoothing, or omitting the smoothing) could shift this fraction by several hundredths, comparable to the offset from the TNG50 median shown in Fig. 10. The paper should include robustness tests against such alternative reconstructions before claiming the offset is significant.
  3. [Footnote 9 and §5] The MAAPs exclude the stellar halo, which is old and would add to the early mass budget; footnote 9 explicitly notes this limitation, but the paper does not quantify its impact on the mass assembly history. Section 5 adds the Gaia-Enceladus progenitor and Sagittarius dwarf contributions (totaling ~2.1e9 Msun) and shows that their effect on colors is negligible, but the effect on the cumulative stellar mass at z~1-1.5 is not shown. Since the missing halo mass is predominantly old, omitting it biases the inferred early-time cumulative fraction. The authors should estimate the maximum plausible halo stellar mass (e.g., from literature values) and demonstrate that its inclusion or exclusion does not change the offset between the MW and TNG50 analogs.
minor comments (4)
  1. [§7, Summary] Typo: "investiage" should be "investigate".
  2. [§3.3] Typo: "psuedo-observables" should be "pseudo-observables".
  3. [§2.3.1] The sentence "the 'dip' in this P SFR profile inside ~5 kpc" contains a stray "P" before "SFR"; also the comparison of metallicity gradient values from Genovali et al. (2014), Wang et al. (2023), Balser et al. (2011), Esteban et al. (2017), Daflon & Cunha (2004), and Boardman et al. (2020b) would benefit from a table or a clear statement of which gradient is used for the adopted value of -0.04 dex/kpc.
  4. [§6.1] In footnote 15, the URL contains a typo: "subhhalos" should be "subhalos".

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the MW track is empirical and compared against independent TNG50 simulations; the main caveats are data assumptions and same-author input, not circular reduction.

full rationale

The paper's derivation chain is not circular. The central input is a set of empirical MAAP density profiles (Sec. 2.2), attributed to the companion paper by J. Imig (in prep), a co-author. These profiles are fitted to APOGEE star counts and are an input data product, not a restatement of the paper's conclusions. The MW's present-day mass and colors are then computed by summing MAAP masses and SSP magnitudes (Secs. 2.4-2.6), and the 'de-aging' procedure in Sec. 4.1 is a bookkeeping of the same age distribution; it is a reconstruction, not an independent prediction. The key comparative claim about earlier-than-average stellar mass assembly is tested against TNG50 analogs (Secs. 6.1-6.2), and the MW properties are separately checked against external literature values (Licquia & Newman 2015; Licquia et al. 2015; Fielder et al. 2021; Rojas-Arriagada et al. 2020; etc.), so the main conclusion is not fitted from the comparison sample. The paper also transparently states its ad hoc choices: ~48% of MAAP bins lack converged fits, unfitted regions are set to zero by visual inspection and then Gaussian-smoothed (Sec. 2.3.3), the inner-Galaxy density is capped for only one supersolar bin (Sec. 2.3.2), and the MAAPs exclude the stellar halo (Footnote 9). These are robustness limitations and model assumptions that could shift the quantitative assembly history, but they do not make the derivation equivalent to its inputs by construction. The score of 2 reflects the load-bearing reliance on an unpublished same-author companion paper and the hand-tuned gap-filling, not a circular logical reduction.

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

The central estimates rest on unpublished MAAP fits, standard stellar population models, and several hand-set corrections for unfitted or poorly sampled MAAPs; no new physical entities are introduced.

free parameters (7)
  • Inner Galaxy density cap radius = RGC < 2.5 kpc
    Applied to supersolar ([M/H]=+0.45, age>2 Gyr) MAAPs to prevent overestimated inner Galaxy masses from the simple broken-exponential profile; affects the bulge MDF and total mass.
  • Gaussian smoothing scale, low-alpha MAAPs = sigma=(0.5,0.5) in the (age, [M/H]) grid
    Chosen by visual inspection of the age-metallicity mass plane to fill unphysical holes; directly changes the mass and color inputs.
  • Gaussian smoothing scale, high-alpha MAAPs = sigma=(0.25,0.25)
    Chosen by visual inspection; affects high-alpha mass distribution and integrated colors.
  • Zeroed unfitted MAAP regions = Specific [M/H]/age boxes listed in Sec. 2.3.3
    Unfit MAAPs are explicitly set to zero based on visual inspection, then interpolation and smoothing are applied; this directly sets the mass budget in sparsely populated regions of the age-metallicity plane.
  • Young MAAP metallicity gradient = -0.04 dex/kpc
    Averaged from Cepheid, MS star, HII region, and OB star measurements; the authors state that using more extreme values does not change their conclusions.
  • Face-on dust normalization = E(B-V)=0.06 at the solar circle, plus AV=0.75 mag for tau<=0.5 Gyr
    Chosen as twice the Galaxia polar E(B-V), with disk scalelength 4.5 kpc and scaleheight 0.14 kpc; affects integrated colors and therefore analog selection.
  • TNG50 analog selection thresholds = +/-0.2 dex in stellar mass and +/-0.15 dex in (g-r)0 color
    Chosen as roughly 2 sigma of the MW measurements; determines the analog samples and all downstream comparison statistics.
assumptions (7)
  • domain assumption The J. Imig (in prep) MAAP density profiles correctly describe the intrinsic spatial distribution of MW stellar populations after APOGEE selection-function correction.
    Used as the sole empirical foundation for MAAP stellar masses; profiles are not independently published or reproducible here.
  • domain assumption PARSEC isochrones and a Kroupa IMF correctly convert giant-branch star counts to stellar masses and SSP luminosities, and adopted initial-final mass relations give correct remnant masses.
    Standard stellar population modeling, but any systematic offset propagates to total mass and colors.
  • domain assumption DistMass age estimates are unbiased enough for 0.1 dex age bins; for low-metallicity high-alpha MAAPs the age distribution is assumed equal to that of the -0.65<[M/H]<-0.3 bin.
    The age distribution is the backbone of the de-aging procedure and assembly history.
  • domain assumption The MW's SFR over the past Gyr is constant and follows the Elia et al. (2022) radial profile, with a constant metallicity gradient of -0.04 dex/kpc.
    Used to add young MAAPs below the APOGEE red-giant age limit; authors test some extremes and find no qualitative change.
  • domain assumption When de-aging the MW, the present-day radial mass distribution of each MAAP and the present-day dust disk can be used at earlier times.
    Explicitly acknowledged as a limitation in Sec. 4.1; radial migration and ISM evolution are not modeled, so early-time colors carry extra uncertainty.
  • domain assumption TNG50 subhalo stellar masses and dust-free colors are directly comparable, without offset correction, to the observationally derived MW masses and PARSEC colors.
    Analog selection and reported mass/color offsets assume the TNG photometric system matches the observed system; no calibration is described.
  • domain assumption The Galaxia synthetic Milky Way model is accurate enough to assert that the MAAP grid covers roughly 95% of MW stars.
    Used in Sec. 2.2 to justify the completeness of the MAAP coverage.

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Cite this review

Pith. "Pith review of Watching our Galaxy Grow Up: The Mass and Color Evolution of the Milky Way." pith.science (2026). https://pith.science/paper/SYJIIVF7

@misc{pith2026250717637,
  author       = {Pith},
  title        = {Pith review of: Watching our Galaxy Grow Up: The Mass and Color Evolution of the Milky Way},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYJIIVF7}},
  note         = {Machine review of arXiv:2507.17637}
}
read the original abstract

Using our rich observations within the Milky Way to better understand galaxy evolution requires understanding what the Milky Way looks like "as a galaxy" -- that is, its "true" shape and abundance profiles (unskewed by observational biases), signatures of past mergers and significant accretion events, and even its total stellar mass and integrated SED, which have historically been difficult to constrain. We present a new approach to determining the Milky Way's integrated mass and colors, using recent measurements of the intrinsic density profiles of stellar populations spanning nearly 13 Gyr in time and 1.5 dex in metallicity (representing nearly all of the Galaxy's stars). We trace the evolution of the Milky Way in various diagnostic spaces, explore the impact of specific events on the present-day Milky Way's integrated properties, and use TNG50 simulations to identify "young" Galactic analogs and their eventual fates, compared to the real Milky Way's path. From the simulation comparisons, we find strong evidence for an earlier-than-average stellar mass assembly of the MW, and that present-day MW analogs follow a similar growth history, albeit at slightly later times; we also find that analogs of the early MW are in no way guaranteed to follow the MW's subsequent path. This empirical study offers new constraints on our "Galaxy as a galaxy" -- today and across cosmic time -- and on its place in the general galactic population.

Figures

Figures reproduced from arXiv: 2507.17637 by the authors.

Figure 1
Figure 1. Left, center: A sample MAAP density profile (§2.2), with the parameterization of Eq. 1. This particular example is for the low-[α/M] MAAP with [M/H] = −0.05 and log τ /yr = 9.75, which has Rbreak = 8.7 kpc, hR,in = 5.3 kpc, hR,out = 1.1 kpc, hZ⊙ = 0.3 kpc, and Aflare = 0.03 (see §2.2 for the definition of these parameters). The color indicates the log of the number density integrated through ZGC (left) and YGC (righ… view at source ↗
Figure 2
Figure 2. MAAP stellar masses in the age–metallicity plane, with low-[α/M] populations on top and high-[α/M] populations on the bottom. The wide outlined boxes show the span of the original MAAPs, with black solid outlines (original fits from J. Imig (in prep) ; §2.2), gray solid outlines (the “young” MAAPs with τ < 1 Gyr; §2.3.1), or black dotted outlines (interpolated/extrapolated MAAPs; §2.3.3). The thin vertical white lin… view at source ↗
Figure 3
Figure 3. Comparison between the stellar masses and (g − r) color for the present day MW as derived in this work (§3; large yellow square and star), in Fielder et al. (2021, gold triangle), and in Licquia et al. (2015, maroon circle). All estimates are comfortably within the “green valley” region indicated by the green band (as defined in Mendel et al. 2013). The background gray contours indicate the distribution of near-face… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Left: The mass-weighted MDFs (scaled to unity at the peaks) derived from the MAAPs in this work (§3.2) for the inner Galaxy (RGC ≤ 3.5 kpc; green), the solar circle (7.5 ≤ RGC ≤ 8.5 kpc; orange), and an annulus of the outer disk (15 ≤ RGC ≤ 17 kpc; purple). These inclu…
Figure 5
Figure 5. Figure 5: The radial metallicity gradient inferred from the MAAPs in this work (§3.2), weighted by bolometric lu￾minosity (thick black line) and by stellar mass (hot pink line). The green, orange, and purple dashed lines indicate the Lbol-weighted metallicities for MAAPs with ag…
Figure 6
Figure 6. Figure 6: Mean star formation rate (SFR) as a function of time (§3.3), inferred from the distribution of mass as a function of age in the inner Galaxy (RGC ≤ 3.5 kpc; green), the solar circle (7.5 ≤ RGC ≤ 8.5 kpc; orange), and an annulus of the outer disk (15 ≤ RGC ≤ 17 kpc; pur…
Figure 7
Figure 7. Figure 7: Integrated, stellar-mass-weighted metallicities over time (§4.2). Top: A gaussian KDE of the integrated, mass-weighted MDF at different redshifts. All have been scaled to pass through unity at solar metallicity, to highlight the shape change rather than absolute values…
Figure 8
Figure 8. Figure 8: Color evolution of the MW with and without including stars belonging to Gaia-Enceladus and Sagittarius (§5). Left: MAAP masses in the age–metallicity plane (akin to [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: The evolving M∗ − (g − r)0 distributions of TNG50 “Milky Way analogs”, selected as analogs at different points in the MW’s history (§6.1). In each panel, the large square indicates our inferred MW mass and color at the given redshift (§4.2), and the contours describe t…
Figure 10
Figure 10. Figure 10: The mass assembly history of our inferred MW (black points) and its TNG50 analogs selected at different redshifts (§6.1). On the left is shown the buildup in absolute mass, and on the right is the cumulative assembly. See §6.2 for details. Additional analyses of simul…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Galactic Self-Portrait: Density Structure and Integrated Properties of the Milky Way Disk

    astro-ph.GA 2025-07 conditional novelty 6.0 of 10

    The Milky Way disk has a stellar mass of 52.7 billion solar masses, a 2.37 kpc scale length, and a present-day (g-r) color of 0.72, indicating a red spiral in the green valley for the last 3 Gyr.

Reference graph

Works this paper leans on

156 extracted references · 40 canonical work pages · cited by 1 Pith paper

  1. [1]

    2022, ApJS, 259, 35

    Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35

  2. [2]

    P., Alexandroff, R., Allende Prieto, C., et al

    Ahn, C. P., Alexandroff, R., Allende Prieto, C., et al. 2014, ApJS, 211, 17

  3. [3]

    F., Argudo-Fern´ andez, M., et al

    Almeida, A., Anderson, S. F., Argudo-Fern´ andez, M., et al. 2023, ApJS, 267, 44

  4. [4]

    X., et al

    Anders, F., Chiappini, C., Santiago, B. X., et al. 2014, A&A, 564, A115 21

  5. [5]

    S., et al

    Anders, F., Chiappini, C., Rodrigues, T. S., et al. 2017, A&A, 597, A30

  6. [6]

    2023, A&A, 678, A158

    Anders, F., Gispert, P., Ratcliffe, B., et al. 2023, A&A, 678, A158

  7. [7]

    E., Springel, V., White, S

    Angulo, R. E., Springel, V., White, S. D. M., et al. 2012, MNRAS, 426, 2046 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

  8. [8]

    K., Balogh, M

    Baldry, I. K., Balogh, M. L., Bower, R. G., et al. 2006, MNRAS, 373, 469

Show all 156 references
  1. [9]

    S., Rood, R

    Balser, D. S., Rood, R. T., Bania, T. M., & Anderson, L. D. 2011, ApJ, 738, 27

  2. [10]

    J., Kay, S

    Barnes, D. J., Kay, S. T., Bah´ e, Y. M., et al. 2017, MNRAS, 471, 1088

  3. [11]

    L., Oelkers, R

    Beaton, R. L., Oelkers, R. J., Hayes, C. R., et al. 2021, AJ, 162, 302

  4. [12]

    2006, A&A, 446, L1

    Montegriffo, P. 2006, A&A, 446, L1

  5. [13]

    R., & Buonanno, R

    Bellazzini, M., Ferraro, F. R., & Buonanno, R. 1999, MNRAS, 304, 633

  6. [14]

    Deason, A. J. 2018, MNRAS, 478, 611

  7. [15]

    2022, MNRAS, 514, 689

    Belokurov, V., & Kravtsov, A. 2022, MNRAS, 514, 689

  8. [16]

    B., Evans, N

    Belokurov, V., Zucker, D. B., Evans, N. W., et al. 2006, ApJL, 642, L137

  9. [17]

    2025, arXiv e-prints, arXiv:2505.01896

    Bhattacharya, S., Arnaboldi, M., Kobayashi, C., Gerhard, O., & Saha, K. 2025, arXiv e-prints, arXiv:2505.01896

  10. [18]

    2016, ARA&A, 54, 529

    Bland-Hawthorn, J., & Gerhard, O. 2016, ARA&A, 54, 529

  11. [19]

    R., & Roweis, S

    Blanton, M. R., & Roweis, S. 2007, AJ, 133, 734

  12. [20]

    R., Bershady, M

    Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, AJ, 154, 28

  13. [21]

    A., et al

    Bonaca, A., Conroy, C., Cargile, P. A., et al. 2020, ApJL, 897, L18

  14. [22]

    S., & Vaughan, Jr., A

    Bowen, I. S., & Vaughan, Jr., A. H. 1973, ApOpt, 12, 1430

  15. [23]

    2012, MNRAS, 427, 127

    Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127

  16. [24]

    A., Law, D

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, The Astrophysical Journal, 798, 7

  17. [25]

    J., Fattahi, A., Callingham, T

    Carrillo, A., Deason, A. J., Fattahi, A., Callingham, T. M., & Grand, R. J. J. 2024, MNRAS, 527, 2165

  18. [26]

    C., Magnier, E

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560

  19. [27]

    A., Rix, H.-W., et al

    Chandra, V., Semenov, V. A., Rix, H.-W., et al. 2024, ApJ, 972, 112

  20. [28]

    2025, arXiv e-prints, arXiv:2501.14089

    Chen, B., Orkney, M., Ting, Y.-S., & Hayden, M. 2025, arXiv e-prints, arXiv:2501.14089

  21. [29]

    2015, MNRAS, 452, 1068

    Chen, Y., Bressan, A., Girardi, L., et al. 2015, MNRAS, 452, 1068

  22. [30]

    2014, MNRAS, 444, 2525

    Chen, Y., Girardi, L., Bressan, A., et al. 2014, MNRAS, 444, 2525

  23. [31]

    R., Cunha, K., et al

    Chou, M.-Y., Majewski, S. R., Cunha, K., et al. 2007, ApJ, 670, 346

  24. [32]

    2012, Research in Astronomy and Astrophysics, 12, 1197

    Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197

  25. [33]

    O’Brien, M

    Cunningham, T., Tremblay, P.-E., & W. O’Brien, M. 2024, MNRAS, 527, 3602

  26. [34]

    2004, ApJ, 617, 1115

    Daflon, S., & Cunha, K. 2004, ApJ, 617, 1115

  27. [35]

    J., Fouesneau, M., Hogg, D

    Dalcanton, J. J., Fouesneau, M., Hogg, D. W., et al. 2015, ApJ, 814, 3 de Boer, T. J. L., Belokurov, V., & Koposov, S. 2015, MNRAS, 451, 3489 De Silva, G. M., Freeman, K. C., Bland-Hawthorn, J., et al. 2015, MNRAS, 449, 2604

  28. [36]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168

  29. [37]

    M., Cunha, K., et al

    Donor, J., Frinchaboy, P. M., Cunha, K., et al. 2020, AJ, 159, 199

  30. [38]

    J., Weinberg, D

    Eisenstein, D. J., Weinberg, D. H., Agol, E., et al. 2011, AJ, 142, 72

  31. [39]

    2022, ApJ, 941, 162

    Elia, D., Molinari, S., Schisano, E., et al. 2022, ApJ, 941, 162

  32. [40]

    2017, MNRAS, 471, 987

    Cipriano, L. 2017, MNRAS, 471, 987

  33. [41]

    E., Garc ´ ıa-Rojas, J., et al

    Esteban, C., M´ endez-Delgado, J. E., Garc ´ ıa-Rojas, J., et al. 2025, arXiv e-prints, arXiv:2501.13586

  34. [42]

    M., Willmer, C

    Faber, S. M., Willmer, C. N. A., Wolf, C., et al. 2007, ApJ, 665, 265

  35. [43]

    2020, MNRAS, 497, 109

    Casagrande, L. 2020, MNRAS, 497, 109

  36. [44]

    2021, MNRAS, 508, 1489

    Casagrande, L. 2021, MNRAS, 508, 1489

  37. [45]

    E., Newman, J

    Fielder, C. E., Newman, J. A., Andrews, B. H., et al. 2021, MNRAS, 508, 4459

  38. [46]

    C., Rice, E

    Filippazzo, J. C., Rice, E. L., Faherty, J., et al. 2015, ApJ, 810, 158 F¨ orster Schreiber, N. M., & Wuyts, S. 2020, ARA&A, 58, 661 22

  39. [47]

    Frankel, N., Rix, H.-W., Ting, Y.-S., Ness, M., & Hogg, D. W. 2018, ApJ, 865, 96

  40. [48]

    2019, ApJ, 884, 99

    Frankel, N., Sanders, J., Rix, H.-W., Ting, Y.-S., & Ness, M. 2019, ApJ, 884, 99

  41. [49]

    2019, Monthly Notices of the Royal Astronomical Society, 489, 5030 Gaia Collaboration, Vallenari, A., Brown, A

    Fraser-McKelvie, A., Merrifield, M., & Arag´ on-Salamanca, A. 2019, Monthly Notices of the Royal Astronomical Society, 489, 5030 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023, A&A, 674, A1

  42. [50]

    2024, A&A, 687, A168

    Gallart, C., Surot, F., Cassisi, S., et al. 2024, A&A, 687, A168

  43. [51]

    Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41 Garc ´ ıa P´ erez, A. E., Allende Prieto, C., Holtzman, J. A., et al. 2016, AJ, 151, 144

  44. [52]

    2014, A&A, 566, A37

    Genovali, K., Lemasle, B., Bono, G., et al. 2014, A&A, 566, A37

  45. [53]

    L., Wetzel, A., Bellardini, M

    Graf, R. L., Wetzel, A., Bellardini, M. A., & Bailin, J. 2024, arXiv e-prints, arXiv:2402.15614

  46. [54]

    Grand, R. J. J., G´ omez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179

  47. [55]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332

  48. [56]

    Hammer, F., Puech, M., Chemin, L., Flores, H., & Lehnert, M. D. 2007, ApJ, 662, 322

  49. [57]

    R., Bovy, J., Holtzman, J

    Hayden, M. R., Bovy, J., Holtzman, J. A., et al. 2015, ApJ, 808, 132

  50. [58]

    R., Majewski, S

    Hayes, C. R., Majewski, S. R., Hasselquist, S., et al. 2020, ApJ, 889, 63

  51. [59]

    2001, MNRAS, 325, 1365

    Haywood, M. 2001, MNRAS, 325, 1365

  52. [60]

    H., et al

    Helmi, A., Babusiaux, C., Koppelman, H. H., et al. 2018, Nature, 563, 85

  53. [61]

    P., Mackereth, J

    Horta, D., Schiavon, R. P., Mackereth, J. T., et al. 2023, MNRAS, 520, 5671

  54. [62]

    C., Sanderson, R., et al

    Horta, D., Cunningham, E. C., Sanderson, R., et al. 2024, MNRAS, 527, 9810

  55. [63]

    Hubble, E. P. 1929, ApJ, 69, 103

  56. [64]

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

  57. [65]

    A., Gilmore, G., & Irwin, M

    Ibata, R. A., Gilmore, G., & Irwin, M. J. 1994, Nature, 370, 194

  58. [66]

    A., et al

    Imig, J., Price, C., Holtzman, J. A., et al. 2023, ApJ, 954, 124

  59. [67]

    2019, ApJL, 878, L11

    Isern, J. 2019, ApJL, 878, L11

  60. [68]

    I., Rich, R

    Johnson, C. I., Rich, R. M., Simion, I. T., et al. 2022, MNRAS, 515, 1469 J¨ onsson, H., Holtzman, J. A., Allende Prieto, C., et al. 2020, AJ, 160, 120

  61. [69]

    A., Kim, H., et al

    Kahre, L., Walterbos, R. A., Kim, H., et al. 2018, ApJ, 855, 133

  62. [70]

    A., Zasowski, G., Rix, H.-W., et al

    Kollmeier, J. A., Zasowski, G., Rix, H.-W., et al. 2017, ArXiv e-prints

  63. [71]

    2001, MNRAS, 322, 231 —

    Kroupa, P. 2001, MNRAS, 322, 231 —. 2002, Science, 295, 82

  64. [72]

    R., McKee, C

    Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227

  65. [73]

    A., Hillenbrand, L

    Kuhn, M. A., Hillenbrand, L. A., Sills, A., Feigelson, E. D., & Getman, K. V. 2019, ApJ, 870, 32

  66. [74]

    Kurtz, D. W. 2022, ARA&A, 60, 31

  67. [75]

    N., & Gunn, J

    Lackner, C. N., & Gunn, J. E. 2012, MNRAS, 421, 2277

  68. [76]

    C., & Sarajedini, A

    Layden, A. C., & Sarajedini, A. 2000, AJ, 119, 1760

  69. [77]

    N., Moran, S

    Leethochawalit, N., Kirby, E. N., Moran, S. M., Ellis, R. S., & Treu, T. 2018, ApJ, 856, 15

  70. [78]

    Lane, R. R. 2023, Nature Astronomy

  71. [79]

    2024, Nature Astronomy, doi: 10.1038/s41550-024-02315-7

    Lian, J., Zasowski, G., Chen, B., et al. 2024, Nature Astronomy, doi: 10.1038/s41550-024-02315-7

  72. [80]

    2020, MNRAS, 497, 3557

    Lian, J., Zasowski, G., Hasselquist, S., et al. 2020, MNRAS, 497, 3557

  73. [81]

    C., & Newman, J

    Licquia, T. C., & Newman, J. A. 2015, ApJ, 806, 96

  74. [82]

    C., Newman, J

    Licquia, T. C., Newman, J. A., & Bershady, M. A. 2016, ApJ, 833, 220

  75. [83]

    C., Newman, J

    Licquia, T. C., Newman, J. A., & Brinchmann, J. 2015, ApJ, 809, 96

  76. [84]

    O., P´ erez-Villegas, A., et al

    Limberg, G., Souza, S. O., P´ erez-Villegas, A., et al. 2022, ApJ, 935, 109

  77. [85]

    J., D’Eugenio, F., Piotrowska, J

    Looser, T. J., D’Eugenio, F., Piotrowska, J. M., et al. 2024, MNRAS, 532, 2832

  78. [86]

    L., Minchev, I., Buck, T., et al

    Lu, Y. L., Minchev, I., Buck, T., et al. 2024, MNRAS, 535, 392

  79. [87]

    T., Crain, R

    Mackereth, J. T., Crain, R. A., Schiavon, R. P., et al. 2018, MNRAS, 477, 5072

  80. [88]

    T., Bovy, J., Schiavon, R

    Mackereth, J. T., Bovy, J., Schiavon, R. P., et al. 2017, MNRAS, 471, 3057

  81. [89]

    T., Schiavon, R

    Mackereth, J. T., Schiavon, R. P., Pfeffer, J., et al. 2019, MNRAS, 482, 3426

  82. [90]

    Ostheimer, J. C. 2003, ApJ, 599, 1082

  83. [91]

    R., Schiavon, R

    Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94

  84. [92]

    2017, ApJ, 835, 77

    Marigo, P., Girardi, L., Bressan, A., et al. 2017, ApJ, 835, 77

  85. [93]

    2009, ApJ, 707, 250

    Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250

  86. [94]

    2021, MNRAS, 508, 2458 23

    Martig, M., Pinna, F., Falc´ on-Barroso, J., et al. 2021, MNRAS, 508, 2458 23

  87. [95]

    T., Simard, L., Ellison, S

    Mendel, J. T., Simard, L., Ellison, S. L., & Patton, D. R. 2013, MNRAS, 429, 2212

  88. [96]

    J., Coil, A

    Mendez, A. J., Coil, A. L., Lotz, J., et al. 2011, ApJ, 736, 110

  89. [97]

    2018, MNRAS, 481, 1645

    Minchev, I., Anders, F., Recio-Blanco, A., et al. 2018, MNRAS, 481, 1645

  90. [98]

    J., Croton, D

    Mutch, S. J., Croton, D. J., & Poole, G. B. 2011, ApJ, 736, 84

  91. [99]

    2019, MNRAS, 488, 1235

    Belokurov, V. 2019, MNRAS, 488, 1235

  92. [100]

    2022, AJ, 164, 85

    Myers, N., Donor, J., Spoo, T., et al. 2022, AJ, 164, 85

  93. [101]

    P., Conroy, C., Bonaca, A., et al

    Naidu, R. P., Conroy, C., Bonaca, A., et al. 2020, ApJ, 901, 48

  94. [102]

    M., Schlaufman, K

    Nataf, D. M., Schlaufman, K. C., Reggiani, H., & Hahn, I. 2024, arXiv e-prints, arXiv:2407.18307

  95. [103]

    C., Rushton, M., et al

    Natale, G., Popescu, C. C., Rushton, M., et al. 2022, MNRAS, 509, 2339

  96. [104]

    2016, PASA, 33, e022

    Ness, M., & Freeman, K. 2016, PASA, 33, e022

  97. [105]

    L., Holtzman, J

    Nidever, D. L., Holtzman, J. A., Allende Prieto, C., et al. 2015, AJ, 150, 173

  98. [106]

    Niederste-Ostholt, M., Belokurov, V., & Evans, N. W. 2012, MNRAS, 422, 207

  99. [107]

    W., & Pe˜ narrubia, J

    Niederste-Ostholt, M., Belokurov, V., Evans, N. W., & Pe˜ narrubia, J. 2010, ApJ, 712, 516

  100. [108]

    2020, A&A, 637, A80

    Shetrone, M. 2020, A&A, 637, A80

  101. [109]

    Pagel, B. E. J., & Patchett, B. E. 1975, MNRAS, 172, 13

  102. [110]

    2019, MNRAS, 485, 5666 —

    Pastorelli, G., Marigo, P., Girardi, L., et al. 2019, MNRAS, 485, 5666 —. 2020, MNRAS, 498, 3283

  103. [111]

    A., Bovy, J., Jaimungal, S., Frankel, N., & Leung, H

    Patil, A. A., Bovy, J., Jaimungal, S., Frankel, N., & Leung, H. W. 2023, MNRAS, 526, 1997

  104. [112]

    2015, Nature, 521, 192

    Peng, Y., Maiolino, R., & Cochrane, R. 2015, Nature, 521, 192

  105. [113]

    2019, MNRAS, 490, 3196

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196

  106. [114]

    H., Elsworth, Y

    Pinsonneault, M. H., Elsworth, Y. P., Tayar, J., et al. 2018, ApJS, 239, 32 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13

  107. [115]

    2015, MNRAS, 448, 713

    Portail, M., Wegg, C., Gerhard, O., & Martinez-Valpuesta, I. 2015, MNRAS, 448, 713

  108. [116]

    J., Menten, K

    Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2014, ApJ, 783, 130

  109. [117]

    2022, ApJ, 941, 45

    Rix, H.-W., Chandra, V., Andrae, R., et al. 2022, ApJ, 941, 45

  110. [118]

    C., Reyl´ e, C., Derri` ere, S., & Picaud, S

    Robin, A. C., Reyl´ e, C., Derri` ere, S., & Picaud, S. 2003, A&A, 409, 523

  111. [119]

    2020, MNRAS, 499, 1037

    Rojas-Arriagada, A., Zasowski, G., Schultheis, M., et al. 2020, MNRAS, 499, 1037

  112. [120]

    J., & Cassisi, S

    Ruiz-Lara, T., Gallart, C., Bernard, E. J., & Cassisi, S. 2020, Nature Astronomy, 4, 965

  113. [121]

    Russell, H. N. 1914, Nature, 93, 252 S´ anchez, S. F., Kennicutt, R. C., Gil de Paz, A., et al. 2012, A&A, 538, A8 S´ anchez, S. F., Barrera-Ballesteros, J. K., Lacerda, E., et al. 2022, ApJS, 262, 36

  114. [122]

    A., Beaton, R

    Santana, F. A., Beaton, R. L., Covey, K. R., et al. 2021, AJ, 162, 303

  115. [123]

    B., Wetzel, A., El-Badry, K., et al

    Santistevan, I. B., Wetzel, A., El-Badry, K., et al. 2020, MNRAS, 497, 747

  116. [124]

    K., Schlafly, E

    Saydjari, A. K., Schlafly, E. F., Lang, D., et al. 2023, ApJS, 264, 28

  117. [125]

    M., Simmons, B

    Schawinski, K., Urry, C. M., Simmons, B. D., et al. 2014, MNRAS, 440, 889

  118. [126]

    F., & Finkbeiner, D

    Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103

  119. [127]

    F., Green, G

    Schlafly, E. F., Green, G. M., Lang, D., et al. 2018, ApJS, 234, 39

  120. [128]

    J., Johnson, J

    Schlesinger, K. J., Johnson, J. A., Rockosi, C. M., et al. 2012, ApJ, 761, 160

  121. [129]

    M., Basu, S., et al

    Schou, J., Antia, H. M., Basu, S., et al. 1998, ApJ, 505, 390

  122. [130]

    M., Kormendy, J., et al

    Shen, J., Rich, R. M., Kormendy, J., et al. 2010, ApJL, 720, L72

  123. [131]

    H., Dotter, A., Majewski, S

    Siegel, M. H., Dotter, A., Majewski, S. R., et al. 2007, ApJL, 667, L57

  124. [132]

    F., Cutri, R

    Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163

  125. [133]

    V., Bizyaev, D., Cunha, K., et al

    Smith, V. V., Bizyaev, D., Cunha, K., et al. 2021, AJ, 161, 254

  126. [134]

    D., Zari, E., Elia, D., et al

    Soler, J. D., Zari, E., Elia, D., et al. 2023, A&A, 678, A95

  127. [135]

    2015, MNRAS, 451, 4086

    Spera, M., Mapelli, M., & Bressan, A. 2015, MNRAS, 451, 4086

  128. [136]

    2023, A&A, 670, A109

    Spitoni, E., Recio-Blanco, A., de Laverny, P., et al. 2023, A&A, 670, A109

  129. [137]

    A., Imig, J., et al

    Stone-Martinez, A., Holtzman, J. A., Imig, J., et al. 2024, AJ, 167, 73

  130. [138]

    2014, MNRAS, 445, 4287

    Tang, J., Bressan, A., Rosenfield, P., et al. 2014, MNRAS, 445, 4287

  131. [139]

    2024, arXiv e-prints, arXiv:2412.12304 24

    Thulasidharan, L., D’Onghia, E., Benjamin, R., et al. 2024, arXiv e-prints, arXiv:2412.12304 24

  132. [140]

    A., Heckman, T

    Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898

  133. [141]

    2020, MNRAS, 491, 5406

    Trussler, J., Maiolino, R., Maraston, C., et al. 2020, MNRAS, 491, 5406

  134. [142]

    2021, MNRAS, 501, 2279

    Vasiliev, E., Belokurov, V., & Erkal, D. 2021, MNRAS, 501, 2279

  135. [143]

    J., Shen, J., & Li, Z.-Y

    Vickers, J. J., Shen, J., & Li, Z.-Y. 2021, ApJ, 922, 189

  136. [144]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

  137. [145]

    2020, Nature Reviews Physics, 2, 42

    Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, Nature Reviews Physics, 2, 42

  138. [146]

    2023, A&A, 674, A129

    Wang, C., Yuan, H., Xiang, M., et al. 2023, A&A, 674, A129

  139. [147]

    H., Holtzman, J

    Weinberg, D. H., Holtzman, J. A., Hasselquist, S., et al. 2019, ApJ, 874, 102

  140. [148]

    R., Hopkins, P

    Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, ApJL, 827, L23

  141. [149]

    C., Hearty, F

    Wilson, J. C., Hearty, F. R., Skrutskie, M. F., et al. 2019, PASP, 131, 055001

  142. [150]

    2019, ApJ, 886, 154

    Yang, C., Xue, X.-X., Li, J., et al. 2019, ApJ, 886, 154

  143. [151]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579

  144. [152]

    2021, ApJ, 912, 106

    Yu, Z., Li, J., Chen, B., et al. 2021, ApJ, 912, 106

  145. [153]

    2023, A&A, 669, A10

    Zari, E., Frankel, N., & Rix, H.-W. 2023, A&A, 669, A10

  146. [154]

    A., Frinchaboy, P

    Zasowski, G., Johnson, J. A., Frinchaboy, P. M., et al. 2013, AJ, 146, 81

  147. [155]

    E., Chojnowski, S

    Zasowski, G., Cohen, R. E., Chojnowski, S. D., et al. 2017, AJ, 154, 198

  148. [156]

    2019, ApJ, 870, 138

    Zasowski, G., Schultheis, M., Hasselquist, S., et al. 2019, ApJ, 870, 138

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