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How precisely can we measure the ages of subgiant and giant stars?

T0 review · 4 major / 6 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Stellar age catalogues can be checked against twin stars born together; this paper uses wide binaries to show that spectroscopic subgiant ages carry honest ~7.5% uncertainties, while a leading photometric catalogue underreports its errors b

desk verdict The Nataf+2024 error underestimation is likely real, but the XR22 validation is fragile—drop one of 12 binaries and σz jumps from 0.81 to 2.50. read the letter →

arxiv 2510.08675 v4 pith:DSB2QRGS submitted 2025-10-09 astro-ph.SR astro-ph.GA

Cheyanne Shariat , Kareem El-Badry , Soumyadeep Bhattacharjee This is my paper
classification astro-ph.SRastro-ph.GA
keywords widebinariesstellaragessubgiantsredgiantsageuncertaintiesisochronefittingspectroscopicabundancesGalacticarchaeology
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tests whether published age estimates for evolved field stars come with realistic uncertainties. Its benchmark is wide binaries—pairs of stars that formed together, so both components must share the same age. Comparing the two catalog ages in each pair, normalized by the quoted errors, reveals whether the error bars are honest. The result: subgiant ages anchored to spectroscopic metal and alpha-element abundances agree with their reported 5–10% uncertainties, red giant and red clump ages are reliable but only at 25–30% precision, and a photometric subgiant catalogue underestimates its true uncertainties by a factor of 2–3. A reader should care because Galactic archaeology depends on knowing which age estimates can be trusted and at what precision.

What carries the argument

The central mechanism is the wide-binary clock: a catalogue of roughly 1.6 million wide binaries within 5 kiloparsecs, built by extending a previous 1-kiloparsec sample, supplies pairs whose members are coeval and share the same initial composition. The test statistic is the uncertainty-normalized age difference z, whose scatter σz should equal 1 if quoted errors are realistic; σz greater than 1 means errors are underestimated, and σz less than 1 means they are overestimated. Each binary thus becomes an independent, model-free calibration experiment for an age catalogue.

What would settle it

Recompute σz for the photometric subgiant sample after removing all pairs with chance-alignment probability R greater than 5%; if σz drops to about 1, the claimed 2–3 times error underestimation depends on pairs that may not be coeval.

Watch

Extended reading notes

Core claim

If two stars formed together, any difference in their catalog ages is a direct measurement of the true uncertainty in those age estimates. Defining z as the age difference divided by the quadrature sum of the quoted errors, the paper finds a scatter of σz ≈ 0.8 for a spectroscopic subgiant sample (after removing one system whose metallicity disagrees at the 3.25σ level), σz ≈ 0.75–1.1 for red giants and red clump stars, and σz ≈ 2.2 for a quality-controlled photometric subgiant sample—meaning that catalogue's errors are underestimated by roughly 2–3 times. The median fractional age uncertainty is 7.5% for the spectroscopic subgiants and 25–30% for the giants. The paper concludes that accurat

Load-bearing premise

That every star pair in the sample is genuinely a binary—two stars born at the same time with the same composition—rather than a chance alignment of unrelated stars.

Editorial extensions

If this is right

  • Subgiant ages derived with spectroscopic metal and alpha-element abundances can credibly reach fractional uncertainties of 5–10%.
  • Photometric subgiant age catalogues should have their formal uncertainties inflated by 2–3 times, or their metallicities replaced with spectroscopic measurements.
  • Red giant and red clump ages from spectroscopic pipelines are calibrated correctly but plateau at roughly 25–30% precision unless asteroseismic or chemical-clock information is added.
  • The extended 5-kiloparsec wide-binary catalogue provides a reusable, model-independent validation set for future age estimates from any survey.

Reading between the lines

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

  • Because shared systematics cancel in a twin-pair comparison, even the validated catalogues carry additional absolute age errors from stellar model physics; the σz ≈ 1 results are lower bounds on total uncertainty, not complete error budgets.
  • A simple robustness test—recomputing σz after excluding pairs with chance-alignment probability above a few percent—would show how much of the photometric catalogue's excess scatter depends on possibly spurious pairs; the paper does not report it.
  • The same wide-binary machinery could be applied to ages derived from Gaia XP spectra as those become available, potentially extending validated subgiant ages to many more stars.
  • The paper's logic implies that abundance-ratio 'chemical clock' ages for giants, while honest about their roughly 28% errors, will not beat isochrone ages until the underlying mixing physics is pinned down.
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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

4 major / 6 minor

Summary. The paper uses Gaia wide binaries as an external, model-independent benchmark to test whether age uncertainties reported by three recent catalogs are realistic. For each binary with two components in the same catalog, the authors compute the normalized age difference z (Eq. 1) and its standard deviation sigma_z, comparing to the expected value of unity. They find: (i) Xiang & Rix (2022) subgiant ages are consistent with reported uncertainties after removing one outlier (sigma_z = 0.81, N=11); (ii) Nataf et al. (2024) photometric subgiant ages underestimate uncertainties (sigma_z = 2.23 for the Primary Sample, 2.73 for the full sample); (iii) Wang et al. (2023) giant ages are consistent (sigma_z = 1.08 full, 0.75 for SNR>50). The conclusions emphasize that spectroscopic abundances are essential for precise subgiant ages and advocate wide binaries as a calibration tool.

Significance. If the claims hold, the paper provides a useful empirical calibration of age uncertainties for three widely used catalogs, and the Nataf et al. (2024) under-estimation result is an important caution for users. The wide-binary approach is genuinely external to the catalogs, and the full sample table (Table 1) enables reproduction. The negative result for Nataf et al. is statistically robust (sigma_z ~ 2.2-2.7 with N=21-61). However, the positive headline result for Xiang & Rix (2022) rests on excluding 1 of 12 systems, and with only N=11 after exclusion the test has very limited power to validate 5-10% uncertainties. The paper also does not quantify contamination from chance-alignment pairs with non-negligible R values. With additional sensitivity analyses, the central claims could be placed on firmer footing.

major comments (4)
  1. [Section 3, Table 1] The claim that Xiang & Rix (2022) subgiant ages are 'generally consistent within their reported uncertainties' depends entirely on post-hoc removal of one binary. Including the 'a' system gives sigma_z = 2.50; excluding it gives sigma_z = 0.81. The stated justification (3.25-sigma [Fe/H] difference, R=2.8e-4) does not rule out a genuine binary with abundance-systematic or unresolved-companion effects. Moreover, with N=11 the standard error on sigma_z is roughly sigma_z/sqrt(2(N-1)) ~ 0.18, so 0.81 is within ~1 sigma of 1. The data therefore do not strongly validate 5-10% uncertainties. Please report the result with the outlier included, and add bootstrap/jackknife or a predefined outlier-rejection rule.
  2. [Table 1, Section 2.2] Chance-alignment contamination is not tested. Several systems used in the main samples have R>0.05 (e.g., W23* rows with R=0.0878 and 0.0510; N24* rows with R=0.0635 and 0.0797). If any of these are optical pairs, they spuriously inflate sigma_z. Since the Nataf et al. under-estimation conclusion is based on sigma_z ~ 2.2-2.7, removing high-R pairs could materially change the inferred inflation factor. Please provide a sensitivity test excluding R>0.05 or R>0.01, or justify why the quoted R values are sufficiently low.
  3. [Table 1, Eq. (1)] At least one W23 row (Gaia DR3 3837150449699070208/3837150518418620032) lists Age2 = 0.00+0.00 with zero lower and upper uncertainty. If included in the sigma_z calculation, the denominator in Eq. (1) is zero and z is undefined; the paper does not state how this entry was treated. Please explain whether such entries were excluded, and confirm that the reported sigma_z = 1.08 for the full W23 sample is robust to their treatment.
  4. [Section 3] The abstract's phrase 'subgiant ages based on spectroscopic metallicities are generally consistent... implying that fractional uncertainties of 5-10% are realistically achievable' overstates what the data can show. For the Wang et al. SNR>50 subsample, sigma_z = 0.75 with N=21 is also statistically indistinguishable from 1. The test as designed can only rule out large under-estimates; it cannot confirm a specific precision floor. Please temper the abstract and conclusions accordingly, or add a formal confidence interval on sigma_z for each subsample.
minor comments (6)
  1. [Abstract / Figure 1 caption] The abstract states photometric subgiant errors are underestimated by 'factors of 2-3', while the Figure 1 caption says 'factors of ~2-5'. These should be reconciled.
  2. [Section 2.2] '1 arcsecond tolerance' is typographically garbled ('1 '' tolerance').
  3. [Figure 1] The top-left panel marks the XR22 outlier with hollow points, but the bottom-left panel does not show the outlier; the reader cannot visually assess its impact. Consider showing it with an open symbol in the bottom panel as well.
  4. [Section 3] The statement 'We find that the catalog of Xiang & Rix (2022) produces the most consistent subgiant ages' appears before the caveat about outlier removal. Soften or reorder so the caveat is not buried.
  5. [Section 4] The discussion of [C/N]-based ages (Section 4.2) is interesting but somewhat disconnected; it reports sigma_z=1.5 for N=7, which is fully consistent with 1 given small N. A brief sentence noting this lack of statistical power would avoid over-interpretation.
  6. [Table 1] The table uses '0.00+0.00' for some ages/uncertainties; please clarify the rounding convention and whether zero values are physical or placeholders.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the wide-binary benchmark is external to the age catalogs being tested.

full rationale

The paper's central statistic, z = (Age1 - Age2)/sqrt(sigma1^2 + sigma2^2), is evaluated using ages and quoted uncertainties drawn from three external catalogs (Xiang & Rix 2022, Nataf et al. 2024, Wang et al. 2023). The wide-binary benchmark is constructed from astrometry-based common-proper-motion pairs (El-Badry et al. 2021), not from the age catalogs, so the tested quantities are not defined in terms of the outcome. No parameter is fitted to force sigma_z ~ 1, and the catalogs' reported uncertainties were not calibrated against wide binaries. The only self-citation that plays a substantive role, El-Badry et al. (2021), provides the input binary catalog rather than the result itself; its selection is independent of stellar ages and of the conclusions. The exclusion of one XR22 outlier is a robustness/statistical-power concern, not a constructional circularity, because it does not redefine z or the null hypothesis. The paper also acknowledges in Section 4.1 that the test is relative and yields a lower limit on absolute uncertainties. No quoted equation reduces to its own inputs, and the claimed validation is not equivalent to the assumptions by construction.

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

No free parameters are fitted; the analysis relies on catalog uncertainties and sample selection thresholds (SNR>50, Primary Sample [Fe/H] range, outlier exclusion). The key assumptions are the physical nature of the wide binaries and the independence of errors.

assumptions (3)
  • domain assumption Components of a wide binary are coeval and share initial chemical composition.
    Used throughout Section 3 to interpret age differences between binary components as measurement noise. Supported by prior literature but not independently verified here.
  • domain assumption The selected pairs are genuine bound binaries rather than chance alignments.
    Entry via the extended wide-binary catalog (Section 2.2). Table 1 lists R values up to ~0.09, but no contamination-sensitivity analysis is performed.
  • domain assumption Quoted uncertainties are treated as independent between the two components.
    Appears in Equation 1 for σz. The paper notes that shared systematics cancel, making the test conservative for detecting underestimation, but this is not quantified.

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Cite this review

Pith. "Pith review of How precisely can we measure the ages of subgiant and giant stars?." pith.science (2026). https://pith.science/paper/DSB2QRGS

@misc{pith2026251008675,
  author       = {Pith},
  title        = {Pith review of: How precisely can we measure the ages of subgiant and giant stars?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DSB2QRGS}},
  note         = {Machine review of arXiv:2510.08675}
}
read the original abstract

Precise stellar ages are fundamental to Galactic archaeology. However, obtaining reliable age estimates and uncertainties for field stars has been a long-standing challenge. We test the fidelity of ages from recent catalogs of giants and subgiants using wide binaries, whose components formed at the same time and thus should have consistent inferred ages. We find that subgiant ages based on spectroscopic metallicities from Xiang & Rix (2022) are generally consistent within their reported uncertainties, implying that fractional uncertainties of 5-10% are realistically achievable. In contrast, we find that published photometric subgiant ages underestimate true uncertainties by factors of 2-3. Spectroscopic age estimates for red giant and red clump stars also show reliable uncertainties, but are generally less precise (25-30%). These results demonstrate that accurate chemical abundance measurements are essential for precise subgiant ages and establish wide binaries as a powerful, model-independent benchmark for calibrating stellar age measurements in the era of large spectroscopic surveys.

Figures

Figures reproduced from arXiv: 2510.08675 by the authors.

Figure 1
Figure 1. Calibrating subgiant and giant star age uncertainties using wide binaries. Top: Kiel diagrams (Teff -log g) for the stars in each sample: Xiang & Rix (2022) subgiants (purple; hollow points show the outlier), Nataf et al. (2024) subgiants (green, red outline for the Primary Sample), and Wang et al. (2023) LAMOST red giants (blue, red outline for SNR> 50). The black points in the background show a randomly selected L… view at source ↗

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Works this paper leans on

64 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

    2023, A&A, 678, A158, doi: 10.1051/0004-6361/202346666

    Anders, F., Gispert, P., Ratcliffe, B., et al. 2023, A&A, 678, A158, doi: 10.1051/0004-6361/202346666

    doi:10.1051/0004-6361/202346666 2023

  2. [2]

    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

  3. [3]

    J., Ag¨ ueros, M

    Andrews, J. J., Ag¨ ueros, M. A., Gianninas, A., et al. 2015, ApJ, 815, 63, doi: 10.1088/0004-637X/815/1/63

    work page doi:10.1088/0004-637x/815/1/63 2015

  4. [4]

    J., Anguiano, B., Chanam´ e, J., et al

    Andrews, J. J., Anguiano, B., Chanam´ e, J., et al. 2019, ApJ, 871, 42, doi: 10.3847/1538-4357/aaf502

    work page doi:10.3847/1538-4357/aaf502 2019

  5. [5]

    Barnes, S. A. 2007, ApJ, 669, 1167, doi: 10.1086/519295

    doi:10.1086/519295 2007

  6. [6]

    R., Mosser, B., Huber, D., et al

    Bedding, T. R., Mosser, B., Huber, D., et al. 2011, Nature, 471, 608, doi: 10.1038/nature09935

    doi:10.1038/nature09935 2011

  7. [7]

    P., Angelou, G

    Bellinger, E. P., Angelou, G. C., Hekker, S., et al. 2016, ApJ, 830, 31, doi: 10.3847/0004-637X/830/1/31

    doi:10.3847/0004-637x/830/1/31 2016

  8. [8]

    A., et al

    Bonaca, A., Conroy, C., Cargile, P. A., et al. 2020, ApJL, 897, L18, doi: 10.3847/2041-8213/ab9caa

    doi:10.3847/2041-8213/ab9caa 2020

Show all 64 references
  1. [9]

    2005, A&A, 442, 635, doi: 10.1051/0004-6361:20053046

    Bonfils, X., Delfosse, X., Udry, S., et al. 2005, A&A, 442, 635, doi: 10.1051/0004-6361:20053046

    2005 doi

  2. [10]

    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

    2012

  3. [11]

    J., et al

    Casagrande, L., Silva Aguirre, V., Schlesinger, K. J., et al. 2016, MNRAS, 455, 987, doi: 10.1093/mnras/stv2320 Chanam´ e, J., & Ram ´ ırez, I. 2012, ApJ, 746, 102, doi: 10.1088/0004-637X/746/1/102

    2016 doi

  4. [12]

    H., Naidu, R

    Conroy, C., Weinberg, D. H., Naidu, R. P., et al. 2022, arXiv e-prints, arXiv:2204.02989, doi: 10.48550/arXiv.2204.02989

    2022 doi arXiv

  5. [13]

    R., Kraus, A

    Deacon, N. R., Kraus, A. L., Mann, A. W., et al. 2016, MNRAS, 455, 4212, doi: 10.1093/mnras/stv2132

    2016 doi

  6. [14]

    J., & Belokurov, V

    Deason, A. J., & Belokurov, V. 2024, NewAR, 99, 101706, doi: 10.1016/j.newar.2024.101706

    2024

  7. [15]

    Demarque, P., Woo, J.-H., Kim, Y.-C., & Yi, S. K. 2004, ApJS, 155, 667, doi: 10.1086/424966

    2004 doi

  8. [16]

    2024, NewAR, 98, 101694, doi: 10.1016/j.newar.2024.101694

    El-Badry, K. 2024, NewAR, 98, 101694, doi: 10.1016/j.newar.2024.101694

    2024

  9. [17]

    El-Badry, K., Rix, H.-W., & Heintz, T. M. 2021, MNRAS, 506, 2269, doi: 10.1093/mnras/stab323

    2021 doi

  10. [18]

    Elsworth, Y., Hekker, S., Basu, S., & Davies, G. R. 2017, MNRAS, 466, 3344, doi: 10.1093/mnras/stw3288 Garc´ es, A., Catal´ an, S., & Ribas, I. 2011, A&A, 531, A7, doi: 10.1051/0004-6361/201116775

    2017 doi

  11. [19]

    A., & Janes, K

    Gruner, D., Barnes, S. A., & Janes, K. A. 2023, A&A, 675, A180, doi: 10.1051/0004-6361/202346590

    2023 doi

  12. [20]

    2020, MNRAS, 492, 1164, doi: 10.1093/mnras/stz3132

    Hawkins, K., Lucey, M., Ting, Y.-S., et al. 2020, MNRAS, 492, 1164, doi: 10.1093/mnras/stz3132

    2020 doi

  13. [21]

    2020, ARA&A, 58, 205, doi: 10.1146/annurev-astro-032620-021917

    Helmi, A. 2020, ARA&A, 58, 205, doi: 10.1146/annurev-astro-032620-021917

    2020 doi

  14. [22]

    A., Littlefair, S

    Hollands, M. A., Littlefair, S. P., & Parsons, S. G. 2024, MNRAS, 527, 9061, doi: 10.1093/mnras/stad3729

    2024 doi

  15. [23]

    Kuszlewicz, J. S. 2020, MNRAS, 499, 2445, doi: 10.1093/mnras/staa2853

    2020 doi

  16. [24]

    A., & Apps, K

    Johnson, J. A., & Apps, K. 2009, ApJ, 699, 933, doi: 10.1088/0004-637X/699/2/933 Precision of subgiant and giant star ages11

    2009 doi

  17. [25]

    L., & Hillenbrand, L

    Kraus, A. L., & Hillenbrand, L. A. 2009, ApJ, 704, 531, doi: 10.1088/0004-637X/704/1/531

    2009 doi

  18. [26]

    2024, Research Notes of the American Astronomical Society, 8, 132, doi: 10.3847/2515-5172/ad4a7c L´ epine, S., & Bongiorno, B

    Lares-Martiz, M., Buzasi, D., Oswalt, T., et al. 2024, Research Notes of the American Astronomical Society, 8, 132, doi: 10.3847/2515-5172/ad4a7c L´ epine, S., & Bongiorno, B. 2007, AJ, 133, 889, doi: 10.1086/510333

    2024 doi

  19. [27]

    W., Bovy, J., Mackereth, J

    Leung, H. W., Bovy, J., Mackereth, J. T., & Miglio, A. 2023, MNRAS, 522, 4577, doi: 10.1093/mnras/stad1272

    2023 doi

  20. [28]

    T., Bovy, J., Leung, H

    Mackereth, J. T., Bovy, J., Leung, H. W., et al. 2019, MNRAS, 489, 176, doi: 10.1093/mnras/stz1521

    2019 doi

  21. [29]

    V., Zacharias, N., & Hennessy, G

    Makarov, V. V., Zacharias, N., & Hennessy, G. S. 2008, ApJ, 687, 566, doi: 10.1086/591638

    2008 doi

  22. [30]

    E., & Hillenbrand, L

    Mamajek, E. E., & Hillenbrand, L. A. 2008, ApJ, 687, 1264, doi: 10.1086/591785

    2008 doi

  23. [31]

    Hilton, E. J. 2013, AJ, 145, 52, doi: 10.1088/0004-6256/145/2/52

    2013 doi

  24. [32]

    2016, MNRAS, 456, 3655, doi: 10.1093/mnras/stv2830

    Martig, M., Fouesneau, M., Rix, H.-W., et al. 2016, MNRAS, 456, 3655, doi: 10.1093/mnras/stv2830

    2016 doi

  25. [33]

    2015, MNRAS, 453, 1855, doi: 10.1093/mnras/stv1731

    Masseron, T., & Gilmore, G. 2015, MNRAS, 453, 1855, doi: 10.1093/mnras/stv1731

    2015 doi

  26. [34]

    M., et al

    Montes, D., Gonz´ alez-Peinado, R., Tabernero, H. M., et al. 2018, MNRAS, 479, 1332, doi: 10.1093/mnras/sty1295

    2018 doi

  27. [35]

    M., Tayar, J., & Claytor, Z

    Morales, L. M., Tayar, J., & Claytor, Z. R. 2025, ApJ, 986, 229, doi: 10.3847/1538-4357/add2f5

    2025 doi

  28. [36]

    Morton, T. D. 2015, isochrones: Stellar model grid package, Astrophysics Source Code Library, record ascl:1503.010. http://ascl.net/1503.010

    2015

  29. [37]

    M., Schlaufman, K

    Nataf, D. M., Schlaufman, K. C., Reggiani, H., & Hahn, I. 2024, ApJ, 976, 87, doi: 10.3847/1538-4357/ad7c4e

    2024 doi

  30. [38]

    W., Rix, H

    Ness, M., Hogg, D. W., Rix, H. W., et al. 2016, ApJ, 823, 114, doi: 10.3847/0004-637X/823/2/114

    2016 doi

  31. [39]

    Nissen, P. E. 2015, A&A, 579, A52, doi: 10.1051/0004-6361/201526269

    2015 doi

  32. [40]

    2022, ApJ, 930, 36, doi: 10.3847/1538-4357/ac6035

    Otani, T., von Hippel, T., Buzasi, D., et al. 2022, ApJ, 930, 36, doi: 10.3847/1538-4357/ac6035

    2022 doi

  33. [41]

    Pagel, B. E. J. 2009, Nucleosynthesis and Chemical Evolution of Galaxies

    2009

  34. [42]

    H., Elsworth, Y., Epstein, C., et al

    Pinsonneault, M. H., Elsworth, Y., Epstein, C., et al. 2014, ApJS, 215, 19, doi: 10.1088/0067-0049/215/2/19

    2014 doi

  35. [43]

    Queiroz, A. B. A., Anders, F., Santiago, B. X., et al. 2018, MNRAS, 476, 2556, doi: 10.1093/mnras/sty330

    2018 doi

  36. [44]

    2016, MNRAS, 463, 1137, doi: 10.1093/mnras/stw2021

    Rebassa-Mansergas, A., Anguiano, B., Garc ´ ıa-Berro, E., et al. 2016, MNRAS, 463, 1137, doi: 10.1093/mnras/stw2021

    2016 doi

  37. [45]

    D., Pinsonneault, M

    Roberts, J. D., Pinsonneault, M. H., Johnson, J. A., et al. 2024, MNRAS, 530, 149, doi: 10.1093/mnras/stae820

    2024 doi

  38. [46]

    R., Muirhead, P

    Rojas-Ayala, B., Covey, K. R., Muirhead, P. S., & Lloyd, J. P. 2010, ApJL, 720, L113, doi: 10.1088/2041-8205/720/1/L113

    2010 doi

  39. [47]

    L., & Das, P

    Sanders, J. L., & Das, P. 2018, MNRAS, 481, 4093, doi: 10.1093/mnras/sty2490

    2018 doi

  40. [48]

    A., et al

    Shetrone, M., Tayar, J., Johnson, J. A., et al. 2019, ApJ, 872, 137, doi: 10.3847/1538-4357/aaff66

    2019 doi

  41. [49]

    2023, MNRAS, 523, 5947, doi: 10.1093/mnras/stad1803

    Silva-Beyer, J., Godoy-Rivera, D., & Chanam´ e, J. 2023, MNRAS, 523, 5947, doi: 10.1093/mnras/stad1803

    2023 doi

  42. [50]

    Soderblom, D. R. 2010, ARA&A, 48, 581, doi: 10.1146/annurev-astro-081309-130806

    2010 doi

  43. [51]

    R., et al

    Stello, D., Huber, D., Bedding, T. R., et al. 2013, ApJL, 765, L41, doi: 10.1088/2041-8205/765/2/L41

    2013 doi

  44. [52]

    A., Imig, J., et al

    Stone-Martinez, A., Holtzman, J. A., Imig, J., et al. 2024, AJ, 167, 73, doi: 10.3847/1538-3881/ad12a6

    2024 doi

  45. [53]

    A., Lu, Y., et al

    Stone-Martinez, A., Holtzman, J. A., Lu, Y., et al. 2025, AJ, 170, 66, doi: 10.3847/1538-3881/addd18

    2025 doi

  46. [54]

    2018, ApJL, 858, L7, doi: 10.3847/2041-8213/aabf8e

    Ting, Y.-S., Hawkins, K., & Rix, H.-W. 2018, ApJL, 858, L7, doi: 10.3847/2041-8213/aabf8e

    2018 doi

  47. [55]

    Twarog, B. A. 1980, ApJ, 242, 242, doi: 10.1086/158460

    1980 doi

  48. [56]

    2016, A&A, 588, A87, doi: 10.1051/0004-6361/201527259

    Vrard, M., Mosser, B., & Samadi, R. 2016, A&A, 588, A87, doi: 10.1051/0004-6361/201527259

    2016 doi

  49. [57]

    2023, A&A, 675, A26, doi: 10.1051/0004-6361/202245809

    Wang, C., Huang, Y., Zhou, Y., & Zhang, H. 2023, A&A, 675, A26, doi: 10.1051/0004-6361/202245809

    2023 doi

  50. [58]

    2025, ApJS, 280, 13, doi: 10.3847/1538-4365/aded16

    Wang, J.-H., Xiang, M., Zhang, M., et al. 2025, ApJS, 280, 13, doi: 10.3847/1538-4365/aded16

    2025 doi

  51. [59]

    2019, MNRAS, 484, 5315, doi: 10.1093/mnras/stz256

    Wu, Y., Xiang, M., Zhao, G., et al. 2019, MNRAS, 484, 5315, doi: 10.1093/mnras/stz256

    2019 doi

  52. [60]

    2022, Nature, 603, 599, doi: 10.1038/s41586-022-04496-5

    Xiang, M., & Rix, H.-W. 2022, Nature, 603, 599, doi: 10.1038/s41586-022-04496-5

    2022 doi

  53. [61]

    2017, ApJS, 232, 2, doi: 10.3847/1538-4365/aa80e4

    Xiang, M., Liu, X., Shi, J., et al. 2017, ApJS, 232, 2, doi: 10.3847/1538-4365/aa80e4

    2017 doi

  54. [62]

    2001, ApJS, 136, 417, doi: 10.1086/321795

    Yi, S., Demarque, P., Kim, Y.-C., et al. 2001, ApJS, 136, 417, doi: 10.1086/321795

    2001 doi

  55. [63]

    M., Chaboyer, B., Boylan-Kolchin, M., Weisz, D

    Ying, J. M., Chaboyer, B., Boylan-Kolchin, M., Weisz, D. R., & Goebel-Bain, R. 2025, ApJ, 987, 52, doi: 10.3847/1538-4357/add471

    2025 doi

  56. [64]

    2012, ApJ, 746, 144, doi: 10.1088/0004-637X/746/2/144

    Zhao, G. 2012, ApJ, 746, 144, doi: 10.1088/0004-637X/746/2/144

    2012 doi

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