Pith. sign in

REVIEW 3 major objections 5 minor 59 references

They Won't Be Giants: Missing Metal-Rich RGB Stars in Gaia Data Indicate Truncated Stellar Evolution

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read The brightest metal-rich giants are missing from the red giant branch in Gaia data, and standard stellar models cannot explain their absence.

desk verdict A clean multi-survey deficit of luminous RGB stars above [Fe/H]>0.4 that is not predicted by synthetic models, but the authors rightly concede a metallicity-label bias cannot yet be ruled out. read the letter →

arxiv 2608.06204 v1 pith:LBKEJQZ3 submitted 2026-08-06 astro-ph.SR

Yuxi Lu , Madeline Howell , Marc H. Pinsonneault , Andrew R. Casey , José G. Fernández-Trincado , José Eduardo Méndez Delgado This is my paper
classification astro-ph.SR
keywords GiantstarsStellarmasslossInitialfunctionevolutionredbranchmetallicityluminosityultravioletupturn
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Using Gaia photometry and astrometry together with metallicity labels from four large spectroscopic surveys, this paper finds that the most luminous red giant branch stars become progressively rarer as metallicity rises, with a near-disappearance above [Fe/H] > 0.4. The red clump and the fainter part of the giant branch stay essentially unchanged across the same metallicity bins, so the deficit is specific to stars near the tip of the giant branch. Synthetic stellar populations built from standard isochrones, realistic ages, and conventional mass-loss recipes reproduce the overall shape of the observed magnitude distributions but predict a roughly constant fraction of luminous giants with metallicity, so the observed truncation is not captured by current models. After testing distance cuts, extinction corrections, survey-to-survey metallicity offsets, and open-cluster metallicity scales, the authors conclude that none of these systematics explains the trend, and interpret the deficit as evidence for enhanced, likely stochastic mass loss that strips the envelopes of a small fraction of the most metal-rich giants before the helium flash. If correct, the result points to a missing channel in stellar evolution and has consequences for helium white dwarf formation, the ultraviolet upturn in metal-rich galaxies, and the inferred initial mass function of massive ellipticals.

What carries the argument

The argument is carried by the extinction-corrected absolute G-magnitude distribution of giant stars, binned by metallicity, compared between observations and synthetic populations. The decisive quantity is the relative fraction of stars in three evolutionary stages—upper red giant branch and luminous giants (URGB/L.G.), red clump (RC), and lower red giant branch (LRGB)—defined by cuts in (M_G)_0 that are shifted to follow the red clump locus in each metallicity bin. This stage-fraction decomposition is what exposes the deficit: the URGB/L.G. fraction declines with metallicity while the red clump and LRGB fractions remain stable, and the same decomposition applied to the synthetic populations stays flat. The synthetic populations serve as the null model, generated from standard stellar isochrones with a conventional mass-loss prescription, metallicity-dependent ages, Gaussian photometric uncertainties, and the same giant-selection cuts as the data. A secondary check uses the apparent G-band magnitude distributions and color-magnitude diagrams to argue that the missing stars are not an artifact of the satellite's brightness limits.

What would settle it

Measure high-resolution spectra of the brightest stars in the [Fe/H] > 0.4 bins with an analysis calibrated for extreme metal-richness: if their true metallicities are systematically lower than reported, the truncation is a metallicity-scale artifact; if the metallicities hold and no stripped descendants (hot subdwarfs or helium white dwarfs) appear in equal numbers in the same volume, the mass-loss interpretation would be in doubt.

Watch

Extended reading notes

Core claim

The central discovery is a systematic, metallicity-dependent truncation of the bright end of the red giant branch luminosity function. Normalizing extinction-corrected absolute-G-magnitude distributions in 0.1 dex metallicity bins spanning [Fe/H] = -1 to > 0.4, the authors find that the red clump peak at (M_G)_0 ~ 0.5 mag is stable, while the number of stars with (M_G)_0 < 0 falls steeply with metallicity and essentially disappears for the most metal-rich bin. This pattern is present in the large Gaia-based sample and independently in three higher-resolution spectroscopic surveys, and it persists when the sample is restricted to stars within 1 kpc of the Sun, where incompleteness is minimal and the trend is strongest. The deficit is not reproduced by synthetic populations constructed from standard isochrones convolved with realistic age, metallicity, and magnitude uncertainties, which keep the luminous-giant fraction nearly constant. The authors systematically test non-physical explanations—brightness and distance selection, extinction corrections, line-blanketing-induced biases, and metallicity-scale consistency using open clusters—and find no evidence that any of them accounts for the trend. They therefore conclude that the missing luminous giants represent a genuine metallicity-dependent reduction in the number of the most evolved red giants, and note explicitly that they cannot exclude some misclassification of extremely metal-rich giants as more metal-poor in the available sample.

Load-bearing premise

The central result would collapse if the most metal-rich giants are systematically mislabeled as more metal-poor by the spectroscopic and photometric pipelines, a possibility the authors state the available sample cannot robustly rule out.

Editorial extensions

If this is right

  • Standard stellar evolution codes will need a metallicity-dependent mass-loss or envelope-stripping mechanism at [Fe/H] > 0.4 to match the observed luminosity functions.
  • Because the red clump population stays nearly constant, any such mechanism can act stochastically on only a small fraction of stars, leaving the bulk of the metal-rich population unaffected.
  • The surviving metal-rich giants retain near-standard masses, so asteroseismic samples of upper RGB stars are biased toward the low-mass-loss branch and may undercount the stars that lose their envelopes.
  • Enhanced envelope stripping at high metallicity offers a pathway to produce the low-mass helium white dwarfs observed in old, metal-rich clusters and the hot stellar component implicated in the ultraviolet upturn of metal-rich early-type galaxies.
  • An apparent bottom-heavy initial mass function in massive elliptical galaxies, inferred from integrated light, could be partly mimicked by a deficit of metal-rich giants rather than an intrinsic excess of low-mass stars.

Reading between the lines

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

  • A natural test the paper does not perform: search the volume-limited sample for the stripped descendants (hot subdwarfs and low-mass helium white dwarfs) with kinematics matching the metal-rich population; their numbers should balance the missing giants if the mass-loss interpretation is correct.
  • If binary stripping is the dominant channel, the few surviving luminous metal-rich giants should have an unusually high companion fraction; a radial-velocity or astrometric binary search among the highest-metallicity giants would discriminate between binary and single-star mass loss.
  • The authors' caveat about misclassification could be tested directly by re-observing the brightest stars in the [Fe/H] > 0.4 bin with an abundance analysis tailored to the metal-rich regime; if their true metallicities shift systematically downward, the truncation would weaken or disappear.
  • The reported trend should strengthen with improved parallax precision; re-running the same stage-fraction analysis with future astrometry releases would provide a clean, independent check of the volume-limited result.
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 reports a population-level anomaly in the Milky Way's giant-star census. Using Gaia XP metallicities (Andrae et al. 2023) together with SDSS-V APOGEE, GALAH DR4, and LAMOST DR11, the authors construct extinction-corrected absolute magnitude distributions in [Fe/H] bins from -1 to >0.4 for volume-limited samples within 4, 2, and 1 kpc. They find that the brightest giants ((M_G)_0 < 0) become progressively rarer toward [Fe/H] > 0.4, while the red clump and lower RGB populations do not show a corresponding decrease. PARSEC v1.2S synthetic populations with a metallicity-dependent adopted age reproduce the global shape of the magnitude distributions but not the truncation. The paper argues against distance, extinction, and survey-selection explanations and proposes enhanced RGB mass loss (single-star or binary) as the physical interpretation, with implications for helium white dwarf formation, the UV upturn, and IMF inferences. Section 4.2 explicitly concedes that metallicity misclassification of the most metal-rich giants cannot be ruled out with the available sample.

Significance. If the deficit is real, this is an important empirical constraint on high-metallicity stellar evolution and population synthesis, with consequences for HeWD formation channels, the UV upturn in early-type galaxies, and the interpretation of bottom-heavy IMFs. The analysis has genuine strengths: it uses multiple surveys, volume-limited subsamples, open-cluster metallicity checks, and an external PARSEC benchmark, and the central ratio is not produced by fitting parameters to the deficit. The result is, however, conditional: the physical interpretation rests on the assumption that [Fe/H] labels of cool luminous giants at the metal-rich end are unbiased, an assumption the paper explicitly leaves untested. The paper is therefore a valuable candidate anomaly rather than a fully established evolutionary truncation at this stage.

major comments (3)
  1. [Section 4.2] The paper concedes that "we cannot rule out the possibility that some of the missing extremely metal-rich giants are misclassified as more metal-poor stars." This is the load-bearing assumption because the entire deficit is defined by counts in the [Fe/H] > 0.4 bin at the bright end. The validation checks do not close this gap: NGC 6791 ([Fe/H] ~ 0.33) and NGC 6253 (~0.26) lie below the critical boundary; the Figure 6 cluster test only uses clusters with more than five URGB stars and therefore does not sample the super-metal-rich regime; and the cross-survey residuals at [Fe/H] > 0.3 are dominated by red clump and lower-RGB stars, which are not the population that is missing. Because Gaia XP is trained on APOGEE DR17 and SDSS-V/LAMOST share APOGEE-calibrated scales, survey agreement does not break the degeneracy. An injection-recovery test is required: embed synthetic cool, luminous giants with known [Fe/H] > 0.4 into the target parameter space and report recovery rates as a function of (M_G)_0 and color. Without this test, the reported truncation cannot be distinguished from a luminosity-dependent metallicity-label bias.
  2. [Section 3.1 and Figure 1] The text and Figure 1 compare an open-ended observed metallicity bin ([Fe/H] > 0.4) with a single synthetic bin at [Fe/H] = 0.5. If the luminous-giant fraction varies within the open bin, this is not a like-for-like comparison. The synthetic prediction should be integrated over the same metallicity distribution as the observed open bin, or the observed bin should be restricted to the same narrow range as the synthetic one. The current choice of a single synthetic point makes the magnitude of the reported deficit sensitive to the metallicity distribution inside the open bin and should be justified or replaced.
  3. [Section 2.2 and Figure 3] The synthetic null population is generated with a single adopted age per metallicity from Lu & Pinsonneault (2026), and the authors reason that age cannot explain the deficit because the mean age is roughly constant for [Fe/H] > -0.4. However, the width and shape of the age distribution also control the number of luminous RGB stars at fixed metallicity, and no uncertainty in the age-metallicity relation is propagated into the predicted URGB fraction. Please show that the predicted luminous-giant fraction is insensitive to the age-distribution shape, for example by using the full Lu & Pinsonneault age distribution rather than its mean, before using the synthetic ratio as the null benchmark.
minor comments (5)
  1. [Title and abstract] The draft contains typographical artifacts in the title ('W on't Be Giants', 'T runcated Stellar Evolution') and similar spacing errors elsewhere; please proofread the manuscript.
  2. [Section 2.1 and Section 3.1] Section 2.1 describes LAMOST DR11 as an independent validation, but Section 3.1 states that its abundances are inferred from APOGEE measurements; please clarify the calibration status of the LAMOST metallicities and state explicitly which surveys are genuinely independent.
  3. [Abstract and Section 3.3] The abstract says the red clump and lower RGB populations remain largely unchanged, but Section 3.3 reports that the RC fraction increases with metallicity; please state whether 'unchanged' refers to absolute counts or to relative fractions and reconcile the wording.
  4. [Section 3.3] The error budget perturbs inputs by 0.1 dex or 0.1 mag and shifts boundaries by 0.1 mag; this estimates the response to scatter, not the effect of a systematic migration of stars across the URGB boundary, so please state this limitation explicitly.
  5. [Section 4] The opening paragraph previews a companion asteroseismic paper and reports results ('We find that the average RGB stellar mass is consistent with expectations...') that are not part of this manuscript; either remove this preview or clearly label it as unpublished companion work.

Circularity Check

0 steps flagged · score 1.0 of 10

Central deficit is an empirical comparison against an external PARSEC null model; the only overlapping-author input (ages) is not load-bearing, and the conceded metallicity-label caveat is a robustness limitation, not a circular step.

full rationale

The paper's central claim is a direct empirical comparison: observed absolute-magnitude distributions of giant stars from four surveys are binned in metallicity, and the fraction of luminous upper-RGB stars declines at [Fe/H] > 0.4 while red-clump and lower-RGB fractions do not. No parameter is fitted to produce this deficit, and the PARSEC-based synthetic population is an external null model that reproduces the overall distributions but not the decline. The only input drawn from overlapping-author work is the age-metallicity relation of Lu & Pinsonneault (2026), used to place synthetic populations at realistic ages. That input is not circular in a load-bearing sense: the decline is visible in the observational fractions independently of the synthetic comparison, and the paper shows the synthetic population built with those ages does not predict the decline. The average age is also reported to be nearly constant over the relevant metallicity range, which weakens the possibility that the age input forces the result. Section 4.2 explicitly concedes that systematic misclassification of extremely metal-rich giants as more metal-poor cannot be ruled out; this is an untested alternative explanation and a correctness risk, not a demonstration that the derivation reduces to its inputs. No self-definitional relationship, fitted-input-as-prediction, or uniqueness-imported-from-authors pattern is present. The mild self-citation therefore warrants a low score rather than a circularity finding.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central claim is observational; no inverse-fitting was used to create the deficit. The free parameters listed are modeling choices for the synthetic null population, not adjustments to produce the result. The most fragile inputs are the age-metallicity relation and the assumption of metallicity-scale integrity at the super-solar end.

free parameters (3)
  • Reimers mass-loss scaling eta_Reimers = 0.2 (tested 0.2-0.4)
    Adopted in PARSEC synthetic populations; it affects the predicted number of luminous giants, but the authors state the trends are insensitive to the 0.2-0.4 range.
  • Red clump and RGB boundary offsets = RC intercept offset 1 mag, RC width 0.6 mag, LRGB cutoff 1.5 mag above the LRGB-RC boundary
    Hand-set cuts used to classify URGB/L.G., RC, and LRGB stars. The uncertainty estimate only perturbs boundaries by 0.1 mag, so larger definition changes are not tested.
  • Gaussian uncertainty convolution width = 0.1 mag in both (G_BP-G_RP)_0 and G magnitude
    Applied to synthetic populations to mimic observational errors; a reasonable choice but not varied, so its effect on the synthetic fraction trends is not quantified.
assumptions (5)
  • domain assumption PARSEC v1.2S isochrones accurately model red giant evolution and photometry for [Fe/H] from -1 to +0.5
    The synthetic comparison is the null model; if isochrones are incorrect at super-solar metallicity, the predicted constant URGB fraction is unreliable.
  • domain assumption The age-metallicity distribution from Lu & Pinsonneault (2026) is accurate
    Ages weight the synthetic populations. The paper relies on overlapping authors' prior work and checks only that the mean age is flat for [Fe/H] > -0.4.
  • domain assumption Metallicity scales of Gaia XP, SDSS-V, GALAH, and LAMOST are consistent and unbiased at [Fe/H] > 0.3
    Central to identifying the highest-metallicity bin. Tested with open clusters NGC 6791 and NGC 6253 and cross-survey residuals, but the authors admit misclassification cannot be fully ruled out.
  • domain assumption Gaia and spectroscopic samples are complete for luminous giants at high metallicity within 1-4 kpc after the adopted cuts
    The deficit is inferred from number ratios; volume-limited subsamples help, but bright-end incompleteness or color-dependent selection could mimic the effect.
  • domain assumption The field stellar population follows a Kroupa initial mass function
    Used to build the synthetic populations; a different IMF would change the predicted fraction of luminous giants, though the authors do not test alternatives.
invented entities (1)
  • Potential missing RGB population (extreme mass-loss RGB stars) independent evidence
    purpose: Proposed reservoir of luminous giants that disappear before the helium flash, forming stripped remnants and helium white dwarfs
    Motivated by the observed deficit, by prior helium white dwarf populations in NGC 6791, and by a companion asteroseismic analysis cited as in preparation. No direct detection is made in this paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of They Won't Be Giants: Missing Metal-Rich RGB Stars in Gaia Data Indicate Truncated Stellar Evolution." pith.science (2026). https://pith.science/paper/LBKEJQZ3

@misc{pith2026260806204,
  author       = {Pith},
  title        = {Pith review of: They Won't Be Giants: Missing Metal-Rich RGB Stars in Gaia Data Indicate Truncated Stellar Evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LBKEJQZ3}},
  note         = {Machine review of arXiv:2608.06204}
}
read the original abstract

We investigate the population of luminous red giant branch stars as a function of metallicity using Gaia XP metallicity combined with SDSS-V, GALAH, and LAMOST. After applying uniform selection criteria and extinction corrections, we construct absolute magnitude distributions across metallicity bins spanning [Fe/H] =-1 to >0.4. We find a systematic deficit of luminous giants at high metallicity, while the red clump and lower red giant branch populations remain largely unchanged. This behavior is consistent with enhanced mass loss at high metallicity, arising from either binary interactions or single-star evolution. This trend is robust across multiple surveys and persists within volume-limited subsamples (1-4 kpc), suggesting it is not driven by distance or selection effects. Synthetic stellar populations based on PARSEC isochrones reproduce the overall magnitude distributions but do not predict a decline in luminous giants with metallicity. Tests of potential systematics, including extinction effects and metallicity scale consistency using open clusters, do not account for the observed trend. We also find no evidence that survey-to-survey differences in metallicity drive the observed result. Together, these findings suggest a metallicity-dependent reduction in the number of luminous red giants that is not captured by current models. This result may have implications for stellar evolution at high metallicity, helium white dwarf formation, and the initial mass function as well as the UV upturn in metal-rich galaxies.

Figures

Figures reproduced from arXiv: 2608.06204 by the authors.

Figure 1
Figure 1. Normalized density distribution of extinction-corrected absolute Gaia G-band magnitudes for Gaia XP sample (top left) and three other large-scale surveys (bottom row), as indicated in the subplot titles. The top right plot shows that for the synthetic population generated from PARSEC. Only giant stars are included, selected with (MG)0< 2.8(GBP − GRP)0−0.5. Colors indicate metallicity, binned in 0.1 dex intervals fro… view at source ↗
Figure 2
Figure 2. Comparison demonstrating that the deficit of luminous giant stars is not caused by the Gaia bright￾ness limit. Color–magnitude diagrams (CMDs; left) and extinction-corrected apparent G-band magnitude (G0) dis￾tributions (right) for three metallicity bins: solar (0 < [M/H] < 0.1, top row), metal-rich (0.3 < [M/H] < 0.4, middle row), and super metal-rich ([Fe/H] > 0.4, bottom row). The or￾ange density distributions in… view at source ↗
Figure 3
Figure 3. Top left: Column-normalized age distribution as a function of metallicity, based on 1.5 million LAMOST stars from Lu & Pinsonneault (2026). Selecting only the stars in common with Andrae et al. (2023) does not significantly change the resulting distributions. Red points indicate the mean age in each metallicity bin, with error bars showing the standard deviation. The average age is relatively constant for [Fe/H]> −0… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Fraction of upper red giant branch/luminous giant (URGB/L.G.; top), red clump (RC; middle), and lower red giant branch (LRGB; bottom) stars compared to the total population as a function of metallicity. Results are shown for the Gaia XP sample within 4, 2, and 1 kpc of…
Figure 6
Figure 6. Figure 6: The average and standard deviation of the [M/H] measurements from Andrae et al. (2023) for URGB/L.G. stars in clusters with membership probability > 0.8 from Hunt & Reffert (2024). Only clusters containing more than 5 URGB/L.G. stars are shown. The intrinsic [M/H] scat…
Figure 5
Figure 5. Figure 5: Metallicity reported from various surveys as a function of G magnitude for member stars of the metal-rich clusters NGC 6791 and NGC 6253. The colored outlines and marker shapes indicate the survey of origin, as shown in the legend, while the colors represent GBP − GRP.…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

59 extracted references · 8 canonical work pages

  1. [1]

    2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    doi:10.3847/1538-4365/ac4414 2022

  2. [2]

    R., Kartha, S

    Akhil, K. R., Kartha, S. S., Kizhuprakkat, N., Ujjwal, K., & P, N. 2024, MNRAS, 534, 4063, doi: 10.1093/mnras/stae2382

    work page doi:10.1093/mnras/stae2382 2024

  3. [3]

    2023, ApJS, 267, 8, doi: 10.3847/1538-4365/acd53e

    Andrae, R., Rix, H.-W., & Chandra, V. 2023, ApJS, 267, 8, doi: 10.3847/1538-4365/acd53e

    doi:10.3847/1538-4365/acd53e 2023

  4. [4]

    L., Pinsonneault, M

    Ash, A. L., Pinsonneault, M. H., Vrard, M., & Zinn, J. C. 2025, ApJ, 979, 135, doi: 10.3847/1538-4357/ad9b18 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74

    doi:10.3847/1538-4357/ad9b18 2025

  5. [5]

    S., & Vaughan, Jr., A

    Bowen, I. S., & Vaughan, Jr., A. H. 1973, ApOpt, 12, 1430, doi: 10.1364/AO.12.001430

    doi:10.1364/ao.12.001430 1973

  6. [6]

    2012, MNRAS, 427, 127, doi: 10.1111/j.1365-2966.2012.21948.x

    Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127, doi: 10.1111/j.1365-2966.2012.21948.x

    arXiv 2012

  7. [7]

    M., Kilic, M., Brown, W

    Brown, J. M., Kilic, M., Brown, W. R., & Kenyon, S. J. 2011, ApJ, 730, 67, doi: 10.1088/0004-637X/730/2/67

    doi:10.1088/0004-637x/730/2/67 2011

  8. [8]

    R., Kilic, M., Allende Prieto, C., & Kenyon, S

    Brown, W. R., Kilic, M., Allende Prieto, C., & Kenyon, S. J. 2010, ApJ, 723, 1072, doi: 10.1088/0004-637X/723/2/1072

    doi:10.1088/0004-637x/723/2/1072 2010

Show all 59 references
  1. [9]

    E., et al

    Buder, S., Kos, J., Wang, X. E., et al. 2025, PASA, 42, e051, doi: 10.1017/pasa.2025.26

    2025 doi

  2. [10]

    R., et al

    Castro-Ginard, A., Penoyre, Z., Casey, A. R., et al. 2024, A&A, 688, A1, doi: 10.1051/0004-6361/202450172

    2024 doi

  3. [11]

    2016, arXiv e-prints, arXiv:1603.02754, doi: 10.48550/arXiv.1603.02754

    Chen, T., & Guestrin, C. 2016, arXiv e-prints, arXiv:1603.02754, doi: 10.48550/arXiv.1603.02754

    2016 doi arXiv

  4. [12]

    M., Slob, M., Kriek, M., et al

    Cheng, C. M., Slob, M., Kriek, M., et al. 2026, arXiv e-prints, arXiv:2601.20864, doi: 10.48550/arXiv.2601.20864

    2026 doi

  5. [13]

    D., & Welch, G

    Code, A. D., & Welch, G. A. 1979, ApJ, 228, 95, doi: 10.1086/156825

    1979 doi

  6. [14]

    Conroy, C., & van Dokkum, P. G. 2012, ApJ, 760, 71, doi: 10.1088/0004-637X/760/1/71

    2012 doi

  7. [15]

    2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003

    Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003

    2012 doi

  8. [16]

    2018, A&A, 614, A19, doi: 10.1051/0004-6361/201732327 Gaia Collaboration, Arenou, F., Babusiaux, C., et al

    Danielski, C., Babusiaux, C., Ruiz-Dern, L., Sartoretti, P., & Arenou, F. 2018, A&A, 614, A19, doi: 10.1051/0004-6361/201732327 Gaia Collaboration, Arenou, F., Babusiaux, C., et al. 2023, A&A, 674, A34, doi: 10.1051/0004-6361/202243782

    2018 doi

  9. [17]

    2015, Science, 347, 1126, doi: 10.1126/science.1259063

    Geier, S., F¨ urst, F., Ziegerer, E., et al. 2015, Science, 347, 1126, doi: 10.1126/science.1259063

    2015 doi

  10. [18]

    2002, A&A, 391, 195, doi: 10.1051/0004-6361:20020612

    Girardi, L., Bertelli, G., Bressan, A., et al. 2002, A&A, 391, 195, doi: 10.1051/0004-6361:20020612

    2002 doi

  11. [19]

    2018, The Journal of Open Source Software, 3, 695, doi: 10.21105/joss.00695

    Green, G. 2018, The Journal of Open Source Software, 3, 695, doi: 10.21105/joss.00695

    2018 doi

  12. [20]

    2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362

    Finkbeiner, D. 2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362

    2019 doi

  13. [21]

    Groenewegen, M. A. T. 2006, A&A, 448, 181, doi: 10.1051/0004-6361:20054163

    2006 doi

  14. [22]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332, doi: 10.1086/500975

    2006 doi

  15. [24]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  16. [25]

    Silva, G. M. 2025, MNRAS, 536, 1389, doi: 10.1093/mnras/stae2686

    2025 doi

  17. [26]

    W., Stello, D., & De Silva, G

    Howell, M., Campbell, S. W., Stello, D., & De Silva, G. M. 2022, MNRAS, 515, 3184, doi: 10.1093/mnras/stac1918 —. 2024, MNRAS, 527, 7974, doi: 10.1093/mnras/stad3565

    2022 doi

  18. [27]

    A., Pinsonneault, M

    Howell, M., Johnson, J. A., Pinsonneault, M. H., et al. 2026, arXiv e-prints, arXiv:2604.27828. https://arxiv.org/abs/2604.27828

    2026 arXiv

  19. [28]

    L., & Reffert, S

    Hunt, E. L., & Reffert, S. 2024, A&A, 686, A42, doi: 10.1051/0004-6361/202348662

    2024 doi

  20. [29]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  21. [30]

    2009, A&A, 493, 1081, doi: 10.1051/0004-6361:200810106

    Justham, S., Wolf, C., Podsiadlowski, P., & Han, Z. 2009, A&A, 493, 1081, doi: 10.1051/0004-6361:200810106

    2009 doi

  22. [31]

    O., Kleinman, S

    Kepler, S. O., Kleinman, S. J., Nitta, A., et al. 2007, MNRAS, 375, 1315, doi: 10.1111/j.1365-2966.2006.11388.x 12

    2007

  23. [32]

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

    Kollmeier, J. A., Zasowski, G., Rix, H.-W., et al. 2017, arXiv e-prints, arXiv:1711.03234, doi: 10.48550/arXiv.1711.03234

    2017 doi arXiv

  24. [33]

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

    Kollmeier, J. A., Rix, H.-W., Aerts, C., et al. 2026, AJ, 171, 52, doi: 10.3847/1538-3881/ae0576

    2026 doi

  25. [34]

    2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x —

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x —. 2002, Science, 295, 82, doi: 10.1126/science.1067524

    2001

  26. [35]

    2025, ApJ, 988, 179, doi: 10.3847/1538-4357/ade3c7

    Li, Y. 2025, ApJ, 988, 179, doi: 10.3847/1538-4357/ade3c7

    2025 doi

  27. [36]

    Liebert, J., Bergeron, P., & Holberg, J. B. 2005, ApJS, 156, 47, doi: 10.1086/425738

    2005 doi

  28. [37]

    Lu, Y., & Pinsonneault, M. H. 2026, arXiv e-prints, arXiv:2607.22309, doi: 10.48550/arXiv.2607.22309

    2026 doi arXiv

  29. [38]

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

    Lu, Y. L., Minchev, I., Buck, T., et al. 2024, MNRAS, 535, 392, doi: 10.1093/mnras/stae2364

    2024 doi

  30. [39]

    2008, A&A, 482, 883, doi: 10.1051/0004-6361:20078467

    Marigo, P., Girardi, L., Bressan, A., et al. 2008, A&A, 482, 883, doi: 10.1051/0004-6361:20078467

    2008 doi

  31. [40]

    McDonald, I., & Zijlstra, A. A. 2015, MNRAS, 448, 502, doi: 10.1093/mnras/stv007

    2015 doi

  32. [41]

    2010, in Proceedings of the 9th Python in Science Conference, Vol

    McKinney, W., et al. 2010, in Proceedings of the 9th Python in Science Conference, Vol. 445, Austin, TX, 51–56

    2010

  33. [42]

    2012, MNRAS, 419, 2077, doi: 10.1111/j.1365-2966.2011.19859.x

    Miglio, A., Brogaard, K., Stello, D., et al. 2012, MNRAS, 419, 2077, doi: 10.1111/j.1365-2966.2011.19859.x

    2012

  34. [43]

    2024, MNRAS, 532, 2534, doi: 10.1093/mnras/stae1645

    Munday, J., Pelisoli, I., Tremblay, P.-E., et al. 2024, MNRAS, 532, 2534, doi: 10.1093/mnras/stae1645

    2024 doi

  35. [44]

    2010, Ap&SS, 329, 25, doi: 10.1007/s10509-010-0392-0

    Nelemans, G. 2010, Ap&SS, 329, 25, doi: 10.1007/s10509-010-0392-0

    2010 doi

  36. [45]

    Nelemans, G., & Tauris, T. M. 1998, A&A, 335, L85, doi: 10.48550/arXiv.astro-ph/9806011 OpenAI. 2025, ChatGPT (July 2025 version), https://chat.openai.com

    1998 doi arXiv

  37. [46]

    I., Pera, M

    Perren, G. I., Pera, M. S., Navone, H. D., & V´ azquez, R. A. 2023, MNRAS, 526, 4107, doi: 10.1093/mnras/stad2826

    2023 doi

  38. [47]

    H., Zinn, J

    Pinsonneault, M. H., Zinn, J. C., Tayar, J., et al. 2025, ApJS, 276, 69, doi: 10.3847/1538-4365/ad9fef

    2025 doi

  39. [48]

    M., Sip˝ ocz, B

    Price-Whelan, A. M., Sip˝ ocz, B. M., G¨ unther, H. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    2018 doi

  40. [49]

    1975, Memoires of the Societe Royale des Sciences de Liege, 8, 369

    Reimers, D. 1975, Memoires of the Societe Royale des Sciences de Liege, 8, 369

    1975

  41. [50]

    W., et al

    Riello, M., De Angeli, F., Evans, D. W., et al. 2021, A&A, 649, A3, doi: 10.1051/0004-6361/202039587

    2021 doi

  42. [51]

    2026, arXiv e-prints, arXiv:2601.10800, doi: 10.48550/arXiv.2601.10800

    Howell, M. 2026, arXiv e-prints, arXiv:2601.10800, doi: 10.48550/arXiv.2601.10800

    2026 doi

  43. [52]

    M., Rowan, D

    Saad, S. M., Rowan, D. M., Stanek, K. Z., et al. 2026, arXiv e-prints, arXiv:2607.27328. https://arxiv.org/abs/2607.27328

    2026 arXiv

  44. [53]

    Saio, H., & Jeffery, C. S. 2000, MNRAS, 313, 671, doi: 10.1046/j.1365-8711.2000.03221.x Schr¨ oder, K.-P., & Cuntz, M. 2005, ApJL, 630, L73, doi: 10.1086/491579

    2000

  45. [54]

    A., & Binney, J

    Sellwood, J. A., & Binney, J. J. 2002, MNRAS, 336, 785, doi: 10.1046/j.1365-8711.2002.05806.x

    2002

  46. [55]

    P., Lagioia, E

    Tailo, M., Milone, A. P., Lagioia, E. P., et al. 2020, MNRAS, 498, 5745, doi: 10.1093/mnras/staa2639

    2020 doi

  47. [56]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  48. [57]

    C., Hearty, F

    Wilson, J. C., Hearty, F. R., Skrutskie, M. F., et al. 2019, PASP, 131, 055001, doi: 10.1088/1538-3873/ab0075

    2019 doi

  49. [58]

    M., & Rix, H.-W

    Zhang, X., Green, G. M., & Rix, H.-W. 2023, MNRAS, 524, 1855, doi: 10.1093/mnras/stad1941

    2023 doi

  50. [59]

    D., Jeffery, C

    Zhang, X., Hall, P. D., Jeffery, C. S., & Bi, S. 2018, MNRAS, 474, 427, doi: 10.1093/mnras/stx2747

    2018 doi

  51. [60]

    Zorotovic, M., & Schreiber, M. R. 2017, MNRAS, 466, L63, doi: 10.1093/mnrasl/slw236

    2017 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.