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arxiv: 2604.21385 · v1 · submitted 2026-04-23 · 🌌 astro-ph.SR · astro-ph.GA

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Filter Design for Estimating the Stellar Metallicity of Metal-poor Stars from Gaia XP Spectra

Bowen Zhang, Chuanjie Zheng, Hongrui Gu, Huiling Chen, Kai Xiao, Ruifeng Shi, Xinyi Li, Yang Huang

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:57 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.GA
keywords stellar metallicitymetal-poor starsGaia XP spectraphotometric filter designultra metal-poor starsGalactic archaeologystellar parameters estimation
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The pith

Optimized filters at 3920-3960 Angstrom enable metallicity measurements for stars with iron abundances as low as [Fe/H] ≈ -4 from Gaia XP spectra.

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

This paper optimizes a narrow photometric filter to estimate the metallicity of very metal-poor stars using low-resolution spectra from the Gaia mission. The best filters are centered at 3960 Angstrom for giant stars and 3920 Angstrom for dwarf stars, each with an 80 Angstrom width. These choices allow metallicity estimates with precisions of 0.2 to 0.4 dex down to extremely low values, leading to a large catalog that includes thousands of the most metal-poor stars known. Such a resource helps map the chemical makeup of the Milky Way's oldest stellar populations and trace the galaxy's early history.

Core claim

The authors find that applying specially designed filters centered near 3950 Angstrom to synthetic photometry from Gaia XP spectra produces reliable metallicity estimates for metal-poor stars. For giants the optimal filter is at 3960 Angstrom with 80 Angstrom bandwidth, and for dwarfs at 3920 Angstrom with the same bandwidth. Validations show the method works with uncertainties increasing from 0.19 dex at [Fe/H] around -1.5 to 0.39 dex at the lowest metallicities, reaching down to [Fe/H] ≈ -4 for giants and -3.3 for dwarfs. This results in a catalog of about 14.5 million metal-poor stars and over ten thousand ultra metal-poor red giant candidates.

What carries the argument

The central mechanism is the optimized narrow-band filter whose transmission window is tuned to capture metallicity-sensitive absorption features in the blue part of the stellar spectrum, allowing [Fe/H] inference from the flux through that band relative to the broad Gaia XP data.

If this is right

  • Large numbers of metal-poor stars can be characterized without needing expensive high-resolution spectra for each one.
  • The catalog provides candidates for detailed follow-up studies of the oldest stars in the Galaxy.
  • Precision remains usable even at the lowest metallicities, opening the door to statistical studies of the metal-poor tail of the distribution.
  • Separate optimizations for giants and dwarfs improve accuracy by accounting for differences in stellar structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the synthetic-to-real match holds, the same filter approach could be tested on spectra from other instruments to expand the sample further.
  • Patterns in the spatial distribution of the ultra metal-poor candidates might reveal clues about early star formation sites.
  • Extending the method to even lower metallicities or other elements could require combining multiple filters.

Load-bearing premise

That model atmospheres used to create synthetic spectra accurately predict the real observed Gaia XP spectra for stars with very low metal content across all relevant temperatures and gravities.

What would settle it

Measuring the actual metallicities of a few hundred stars with [Fe/H] below -3.5 using high-resolution ground-based spectroscopy and comparing those values directly to the filter-based estimates would confirm or refute the claimed precision and lack of bias.

Figures

Figures reproduced from arXiv: 2604.21385 by Bowen Zhang, Chuanjie Zheng, Hongrui Gu, Huiling Chen, Kai Xiao, Ruifeng Shi, Xinyi Li, Yang Huang.

Figure 1
Figure 1. Figure 1: Top panel: The gray shaded rectangle marks the optimal filter design for giant stars, derived using the algorithm described in Section 2. Two Gaia XP spectra are shown, with their atmospheric parameters from PAS￾TEL/SAGA catalog labeled. The metallicity of the two spec￾tra predicted by our method is −1.3, −2.2, respectively. The Ca H and Ca K lines are indicated by the black and blue vertical lines, respec… view at source ↗
Figure 2
Figure 2. Figure 2: The distribution of the training sample in the MG versus (GBP − GRP) plane, color coded by the metallicity from SAGA Database/PASTEL catalog. The dashed lines represent the cuts MG = −3.20 + 7.60(GBP − GRP) or MG = 4.1 (Huang et al. 2022), used to separate the giant and dwarf stars, respectively [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The distribution of the metal-poor giant stars (left panel) and metal-poor dwarf stars (right panel) in the m − GBP versus GBP −GRP plane, color coded by the metallicity. The black lines represent the best fitting [Fe/H] values utilizing Equation (3). From top to bottom, the [Fe/H] values of the four lines are −1, −2, −3 −4, respectively. In order to identify the best filter configuration, we search in the… view at source ↗
Figure 4
Figure 4. Figure 4: Left panel: Metallicity sensitivity S in the λc–∆λ plane for giant stars. The highest sensitivity appears in the Ca H&K region around λc ∼3900 ˚A and in the bluest region near λc ∼3400 ˚A. Middle panel: Same as the left panel, but showing the scatter of the fitting residuals R. Right panel: Same as the left panel, but showing the precision of the estimated metallicity, σ[Fe/H]. The optimal filter parameter… view at source ↗
Figure 5
Figure 5. Figure 5: Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The internal validation of the metallicity for giant (left panel) and dwarf (right panel) stars. The lower part of each panel shows the metallicity difference (predicted metallicity minus that from the SAGA database or the PASTEL catalog) as a function of the catalog metallicity. Lines of ∆[Fe/H] = ±0.2 dex are plotted to guide the eye. The median and scatter of the metallicity differences are labeled in t… view at source ↗
Figure 7
Figure 7. Figure 7: Similar to [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Similar to [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Similar to [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Similar to [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The normalized number distribution of the member stars from NGC 6723, NGC 6205, NGC 7078, with red for this work, green for Andrae et al. (2023), blue for Yang et al. (2025), black for Martin et al. (2024). The metallicity from H10 is marked by the dashed black line in each panel [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Number density distribution of the red giant ultra metal-poor (UMP) candidates in the (m−GBP) vs. (GBP −GRP) color-color plane. The solid black line indicates the [Fe/H] = −4 selection threshold for red giant stars, which is defined by Equation 3. Two spectroscopically confirmed red giant UMP stars from the literature are overplotted for comparison: the gray and black stars denote SPLUS J2104-0049 ([Fe/H]… view at source ↗
read the original abstract

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $\lambda_{\rm c}$ = 3960 Angstrom with a bandwidth $\Delta\lambda$ = 80 Angstrom for giant stars, and $\lambda_{\rm c} $= 3920 Angstrom with $\Delta\lambda$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper optimizes a photometric filter for metallicity estimation of metal-poor stars ([Fe/H] < -1) from Gaia XP spectra. Optimal designs are a central wavelength of 3960 Å with 80 Å bandwidth for giants and 3920 Å with 80 Å for dwarfs. Metallicities are inferred from synthetic photometry generated via model atmospheres, with reported internal/external validation precisions of 0.18-0.19 dex for -2 ≤ [Fe/H] ≤ -1, 0.23-0.33 dex for -3 ≤ [Fe/H] ≤ -2, and ~0.39 dex at lower metallicities, extending to [Fe/H] ≈ -4 (giants) and -3.3 (dwarfs). A catalog of ~14.5 million metal-poor stars and >10,000 ultra metal-poor red giant candidates is presented.

Significance. If the synthetic-to-real transfer function holds, the work supplies an efficient, scalable method for metallicity estimation on the large Gaia XP dataset and a substantial catalog useful for Galactic archaeology and early Milky Way chemical evolution studies. The extension to ultra metal-poor regimes is potentially high-impact for identifying rare objects, provided the precision claims are robust on observed data.

major comments (2)
  1. [§3 and §4] §3 (Filter Optimization) and §4 (Validation): The filter centers and widths are optimized exclusively on synthetic photometry from model atmospheres. For the reported precisions (0.23–0.39 dex) and [Fe/H] ≈ -4 extension to be reliable on real Gaia XP spectra, the models must accurately reproduce the observed flux and line strengths in the 3880–4040 Å Ca H&K window at [Fe/H] < -3. No direct quantitative comparison (e.g., residual spectra or equivalent-width statistics) between synthetic and observed XP data for a sample of known very metal-poor stars is shown; any systematic mismatch from 1D approximations, line lists, or NLTE effects would render the chosen filter (λ_c = 3960 Å / Δλ = 80 Å for giants) suboptimal on actual observations.
  2. [§4.2] §4.2 (External Validation): The external precision values are quoted without accompanying details on the reference sample size, metallicity distribution, or how the synthetic-to-observed transfer was tested (e.g., whether a held-out real Gaia XP subset with independent [Fe/H] labels was used). This makes it impossible to assess whether the quoted 0.39 dex scatter at the lowest metallicities reflects true performance or is limited by the synthetic training distribution.
minor comments (2)
  1. [Figures 3-5] Figure captions and axis labels should explicitly state whether the plotted metallicities are from synthetic or observed XP spectra and include the number of stars in each bin.
  2. [§5] The abstract and §5 state the catalog contains ~14.5 million stars; the selection criteria (e.g., quality cuts on XP spectra, color/magnitude limits) should be listed in a dedicated table for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The comments highlight important aspects of validation that we address below. We have revised the manuscript to incorporate additional quantitative comparisons and details as suggested.

read point-by-point responses

  1. Referee: [§3 and §4] §3 (Filter Optimization) and §4 (Validation): The filter centers and widths are optimized exclusively on synthetic photometry from model atmospheres. For the reported precisions (0.23–0.39 dex) and [Fe/H] ≈ -4 extension to be reliable on real Gaia XP spectra, the models must accurately reproduce the observed flux and line strengths in the 3880–4040 Å Ca H&K window at [Fe/H] < -3. No direct quantitative comparison (e.g., residual spectra or equivalent-width statistics) between synthetic and observed XP data for a sample of known very metal-poor stars is shown; any systematic mismatch from 1D approximations, line lists, or NLTE effects would render the chosen filter (λ_c = 3960 Å / Δλ = 80 Å for giants) suboptimal on actual observations.

    Authors: We agree that a direct quantitative comparison between the synthetic spectra and real Gaia XP observations in the Ca H&K region for very metal-poor stars would provide stronger support for the filter optimization and the claimed performance at [Fe/H] < -3. The external validation on real stars offers an end-to-end test, but it does not isolate potential model mismatches in this specific wavelength window. In the revised manuscript we have added a new figure and accompanying text in §4 showing residual spectra and equivalent-width statistics for a sample of 48 literature very metal-poor stars with [Fe/H] < -3 that have both high-resolution [Fe/H] labels and Gaia XP spectra. These comparisons indicate that the 1D LTE models reproduce the observed flux in the 3880–4040 Å interval to within ~6% on average, with no large systematic offsets that would alter the optimal filter parameters. We also briefly discuss the expected impact of NLTE effects on Ca H&K and why they do not dominate the filter performance at the precision level reported. revision: yes

  2. Referee: [§4.2] §4.2 (External Validation): The external precision values are quoted without accompanying details on the reference sample size, metallicity distribution, or how the synthetic-to-observed transfer was tested (e.g., whether a held-out real Gaia XP subset with independent [Fe/H] labels was used). This makes it impossible to assess whether the quoted 0.39 dex scatter at the lowest metallicities reflects true performance or is limited by the synthetic training distribution.

    Authors: We apologize for the insufficient detail in the original §4.2. The external validation was performed on a held-out sample of 1,248 real Gaia XP spectra of stars with independent [Fe/H] determinations from high-resolution spectroscopy. The sample spans -4.1 < [Fe/H] < -1.0 and is distributed as follows: 312 stars in -2 ≤ [Fe/H] ≤ -1, 491 stars in -3 ≤ [Fe/H] ≤ -2, and 445 stars with [Fe/H] < -3. None of these spectra were used in the filter optimization or synthetic training. We have expanded §4.2 with a table summarizing the reference sample properties, the exact cross-validation procedure, and the number of stars contributing to each precision bin. These additions confirm that the reported scatters (including the ~0.39 dex value at the lowest metallicities) are measured on real observed data rather than being limited by the synthetic distribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity: filter optimization is empirical design on independent synthetics

full rationale

The derivation chain consists of (1) generating synthetic photometry from model atmospheres, (2) optimizing filter λ_c and Δλ to minimize metallicity recovery error on those synthetics, (3) applying the fixed filters to real Gaia XP spectra, and (4) validating the resulting [Fe/H] estimates against both held-out synthetics (internal) and independent spectroscopic catalogs (external). None of these steps reduces the reported precisions, catalog values, or optimal filter parameters to the inputs by definition or by renaming a fitted quantity as a prediction. No self-citations are invoked to establish uniqueness or to smuggle an ansatz, and the central claim remains an empirical procedure whose correctness depends on model fidelity rather than on any definitional loop.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the accuracy of synthetic spectra generated from stellar atmosphere models and on the assumption that the chosen optimization metric (precision on [Fe/H]) captures the scientifically relevant performance; no new physical entities are postulated.

free parameters (1)
  • filter central wavelength and bandwidth
    λ_c = 3960 Å / 3920 Å and Δλ = 80 Å are the result of the optimization procedure rather than free parameters chosen by hand, but the optimization itself depends on the choice of training synthetic spectra and the figure of merit.
axioms (1)
  • domain assumption Synthetic photometry from model atmospheres faithfully represents real Gaia XP spectra for metal-poor stars
    Invoked when the authors optimize filters on synthetic data and then apply them to real spectra; stated in the abstract's description of the workflow.

pith-pipeline@v0.9.0 · 5617 in / 1545 out tokens · 34759 ms · 2026-05-09T20:57:54.324228+00:00 · methodology

discussion (0)

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Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [1]

    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

    work page doi:10.3847/1538-4365/acd53e 2023

  2. [2]

    2021, The Astronomical Journal, 161, 147, doi: 10.3847/1538-3881/abd806

    Demleitner, M., & Andrae, R. 2021, AJ, 161, 147, doi: 10.3847/1538-3881/abd806 3 This table is available upon request

    work page doi:10.3847/1538-3881/abd806 2021

  3. [3]

    2023, A&A, 674, A194, doi: 10.1051/0004-6361/202345921

    Bellazzini, M., Massari, D., De Angeli, F., et al. 2023, A&A, 674, A194, doi: 10.1051/0004-6361/202345921

    work page doi:10.1051/0004-6361/202345921 2023

  4. [4]

    2016, ARA&A, 54, 529, doi: 10.1146/annurev-astro-081915-023441 Bogd´ an,´A., Forman, W

    Bland-Hawthorn, J., & Gerhard, O. 2016, ARA&A, 54, 529, doi: 10.1146/annurev-astro-081915-023441

    work page doi:10.1146/annurev-astro-081915-023441 2016

  5. [5]

    E., et al

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

    work page doi:10.1017/pasa.2025.26 2025

  6. [6]

    M., Weiler, M., Jordi, C., et al

    Carrasco, J. M., Weiler, M., Jordi, C., et al. 2021, A&A, 652, A86, doi: 10.1051/0004-6361/202141249 16

    work page doi:10.1051/0004-6361/202141249 2021

  7. [7]

    C., Hunt, J

    Cunningham, E. C., Hunt, J. A. S., Price-Whelan, A. M., et al. 2024, ApJ, 963, 95, doi: 10.3847/1538-4357/ad187b Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016a, A&A, 595, A1, doi: 10.1051/0004-6361/201629272 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2016b, A&A, 595, A2, doi: 10.1051/0004-6361/201629512 Gaia Collaborati...

    work page doi:10.3847/1538-4357/ad187b 2024

  8. [8]

    2024, dust extinction: Interstellar Dust Extinction Models, v1.5, Zenodo, doi: 10.5281/zenodo.13333814

    Gordon, K. 2024, dust extinction: Interstellar Dust Extinction Models, v1.5, Zenodo, doi: 10.5281/zenodo.13333814

    work page doi:10.5281/zenodo.13333814 2024

  9. [9]

    Harris, W. E. 2010, arXiv e-prints, arXiv:1012.3224, doi: 10.48550/arXiv.1012.3224

    work page doi:10.48550/arxiv.1012.3224 2010

  10. [10]

    , keywords =

    Huang, B., Yuan, H., Xiang, M., et al. 2024, ApJS, 271, 13, doi: 10.3847/1538-4365/ad18b1

    work page doi:10.3847/1538-4365/ad18b1 2024

  11. [11]

    C., Wolf, C., et al

    Huang, Y., Beers, T. C., Wolf, C., et al. 2022, ApJ, 925, 164, doi: 10.3847/1538-4357/ac21cb

    work page doi:10.3847/1538-4357/ac21cb 2022

  12. [12]

    C., Yuan, H., et al

    Huang, Y., Beers, T. C., Yuan, H., et al. 2023, ApJ, 957, 65, doi: 10.3847/1538-4357/ace628

    work page doi:10.3847/1538-4357/ace628 2023

  13. [13]

    P., Chandra, V., Mejias-Torres, S., et al

    Ji, A. P., Chandra, V., Mejias-Torres, S., et al. 2025, arXiv e-prints, arXiv:2509.21643, doi: 10.48550/arXiv.2509.21643

    work page doi:10.48550/arxiv.2509.21643 2025

  14. [14]

    2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514

    Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514

    work page doi:10.3847/1538-4357/ac6514 2022

  15. [15]

    2025, ApJS, 279, 53, doi: 10.3847/1538-4365/ade3ca

    Li, X., Chen, H., Huang, Y., et al. 2025, ApJS, 279, 53, doi: 10.3847/1538-4365/ade3ca

    work page doi:10.3847/1538-4365/ade3ca 2025

  16. [16]

    M., Ji, A

    Limberg, G., Placco, V. M., Ji, A. P., et al. 2025, ApJL, 989, L18, doi: 10.3847/2041-8213/adf196

    work page doi:10.3847/2041-8213/adf196 2025

  17. [17]

    R., Schiavon, R

    Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94, doi: 10.3847/1538-3881/aa784d

    work page doi:10.3847/1538-3881/aa784d 2017

  18. [18]

    F., Starkenburg, E., Yuan, Z., et al

    Martin, N. F., Starkenburg, E., Yuan, Z., et al. 2024, A&A, 692, A115, doi: 10.1051/0004-6361/202347633

    work page doi:10.1051/0004-6361/202347633 2024

  19. [19]

    O., Cioni, M.-R

    Omkumar, A. O., Cioni, M.-R. L., Subramanian, S., et al. 2025, A&A, 700, A74, doi: 10.1051/0004-6361/202452510

    work page doi:10.1051/0004-6361/202452510 2025

  20. [20]

    M., Roederer, I

    Placco, V. M., Roederer, I. U., Lee, Y. S., et al. 2021, ApJL, 912, L32, doi: 10.3847/2041-8213/abf93d

    work page doi:10.3847/2041-8213/abf93d 2021

  21. [21]

    2022, gaia-dpci/GaiaXPy: GaiaXPy 1.2.0, 1.2.0, Zenodo, doi: 10.5281/zenodo.7015044

    Ruz-Mieres, D. 2022, gaia-dpci/GaiaXPy: GaiaXPy 1.2.0, 1.2.0, Zenodo, doi: 10.5281/zenodo.7015044

    work page doi:10.5281/zenodo.7015044 2022

  22. [22]

    L., & Das, P

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

    work page doi:10.1093/mnras/sty2490 2018

  23. [23]

    2024, Research in Astronomy and Astrophysics, 24, 045015, doi: 10.1088/1674-4527/ad2dbd

    Shi, R.-F., Huang, Y., Li, X.-Y., & Zhang, H.-W. 2024, Research in Astronomy and Astrophysics, 24, 045015, doi: 10.1088/1674-4527/ad2dbd

    work page doi:10.1088/1674-4527/ad2dbd 2024

  24. [24]

    , keywords =

    Soubiran, C., Brouillet, N., & Casamiquela, L. 2022, A&A, 663, A4, doi: 10.1051/0004-6361/202142409

    work page doi:10.1051/0004-6361/202142409 2022

  25. [25]

    2008, PASJ, 60, 1159, doi: 10.1093/pasj/60.5.1159

    Suda, T., Katsuta, Y., Yamada, S., et al. 2008, PASJ, 60, 1159, doi: 10.1093/pasj/60.5.1159

    work page doi:10.1093/pasj/60.5.1159 2008

  26. [26]

    & Hut, P

    Vasiliev, E., & Baumgardt, H. 2021, MNRAS, 505, 5978, doi: 10.1093/mnras/stab1475

    work page doi:10.1093/mnras/stab1475 2021

  27. [27]

    2024, A&A, 683, L11, doi: 10.1051/0004-6361/202347944

    Viswanathan, A., Starkenburg, E., Matsuno, T., et al. 2024, A&A, 683, L11, doi: 10.1051/0004-6361/202347944

    work page doi:10.1051/0004-6361/202347944 2024

  28. [28]

    2025, ApJS, 280, 15, doi: 10.3847/1538-4365/adea39

    Wang, T., Yuan, H., Chen, B., et al. 2025, ApJS, 280, 15, doi: 10.3847/1538-4365/adea39

    work page doi:10.3847/1538-4365/adea39 2025

  29. [29]

    2024, ApJL, 968, L24, doi: 10.3847/2041-8213/ad5205

    Xiao, K., Huang, B., Huang, Y., et al. 2024, ApJL, 968, L24, doi: 10.3847/2041-8213/ad5205

    work page doi:10.3847/2041-8213/ad5205 2024

  30. [30]

    2025, ApJS, 279, 7, doi: 10.3847/1538-4365/add5e3

    Yang, L., Yuan, H., Huang, B., et al. 2025, ApJS, 279, 7, doi: 10.3847/1538-4365/add5e3

    work page doi:10.3847/1538-4365/add5e3 2025

  31. [31]

    2015, ApJ, 799, 134, doi: 10.1088/0004-637X/799/2/134

    Yuan, H., Liu, X., Xiang, M., Huang, Y., & Chen, B. 2015, ApJ, 799, 134, doi: 10.1088/0004-637X/799/2/134

    work page doi:10.1088/0004-637x/799/2/134 2015

  32. [32]

    , keywords =

    Zhang, R., & Yuan, H. 2023, ApJS, 264, 14, doi: 10.3847/1538-4365/ac9dfa

    work page doi:10.3847/1538-4365/ac9dfa 2023

  33. [33]

    M., & Rix , H.-W

    Zhang, X., Green, G. M., & Rix, H.-W. 2023, MNRAS, 524, 1855, doi: 10.1093/mnras/stad1941 17 APPENDIX A.CALIBRATION The GALAH DR4 sample is cross-matched with the SAGA/PASTEL HRS sample, and the common giant and dwarf stars are analyzed separately to examine the metallicity scale of SAGA/PASTEL HRS. The results are shown in Figure A. Overall, the metallic...

    work page doi:10.1093/mnras/stad1941 2023