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Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne

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Pith's one-line read This paper derives homogeneous atmospheric parameters and 22-element abundances for millions of FGK stars from low-resolution LAMOST spectra, reaching errors of 30 K in Teff, 0.07 dex in log g, and 0.05–0.2 dex in abundances at S/N > 50.

desk verdict A valuable, well-validated 6.4M-star catalog from LAMOST with a fixable but real NLTE inconsistency between [Fe/H] and APOGEE-trained [X/Fe]. read the letter →

arxiv 2506.02763 v2 pith:GHYXBXMT submitted 2025-06-03 astro-ph.SR astro-ph.GA

Meng Zhang , Maosheng Xiang , Yuan-Sen Ting , Anish Mayur Amarsi , Hua-Wei Zhang , Jianrong Shi , Haibo Yuan , Haining Li
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Jiahui Wang Yaqian Wu Tianmin Wu Lanya Mou Hong-liang Yan Jifeng Liu
This is my paper · ORCID
classification astro-ph.SRastro-ph.GA
keywords stellarabundancesLAMOSTDD-Paynelow-resolutionspectroscopyGalacticarchaeologyneuralnetworkss-processelementsr-process
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 claims that a data-driven neural network trained on high-resolution survey labels, regularized by physical model gradients, can turn LAMOST's low-resolution spectra into a homogeneous catalog of atmospheric parameters for 6.4 million FGK stars and abundances of 22 elements for about 3.6 million of them. This matters because Galactic archaeology needs chemical fingerprints in many dimensions for massive samples, and low-resolution surveys observe tens of millions of stars, far more than high-resolution surveys can. The paper further claims that careful calibration—placing Teff on the infrared flux method scale, validating log g with asteroseismology, correcting [Fe/H] for non-LTE effects, and removing temperature trends in [X/Fe] using wide binaries—makes the catalog homogeneous enough to resolve known population structures such as the high- and low-α disks and the accreted halo. If correct, this is the largest homogeneous multi-element abundance catalog to date, and the first low-resolution extraction of nine s- and r-process elements on this scale.

What carries the argument

The central mechanism is DD-Payne, a two-layer neural network spectral model that predicts flux at each wavelength pixel as a function of 25 stellar labels, trained on common stars with APOGEE DR17/GALAH DR3 plus very metal-poor stars and regularized by penalizing deviations from Kurucz ATLAS12/SYNTHE gradient spectra. This lets the model fit heavily blended low-resolution spectra while keeping the label sensitivity physically sensible. The supporting calibration chain—IRFM temperature scale, asteroseismic surface gravity, NLTE-corrected iron, and wide-binary-based temperature-trend removal for abundances—is what converts self-consistent model outputs into a homogeneous, externally anchored catalog.

What would settle it

Take a sample of metal-poor dwarf stars from the catalog with S/N > 50 and [Fe/H] < −2, obtain new high-resolution, NLTE-corrected abundances of Sr, Ba, and Eu; if the differences from the catalog exceed the quoted 0.1–0.2 dex and grow with decreasing [Fe/H], the claimed validity range for [X/Fe] fails.

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Extended reading notes

Core claim

The paper's central claim is that DD-Payne, a neural network trained to map spectra to stellar labels, can transfer labels from high-resolution surveys to LAMOST low-resolution spectra and produce a homogeneous catalog of atmospheric parameters for 6.4 million FGK stars and abundance ratios for 22 elements for nearly 3.6 million stars with S/N > 20. The labels are trained on common stars between LAMOST and APOGEE DR17, LAMOST and GALAH DR3, and 345 very metal-poor stars from Li et al. (2022), with the network regularized by gradient spectra from Kurucz model atmospheres so that the model is physically sensible. The paper reports typical errors of 30 K in Teff, 0.07 dex in log g, and 0.05–0.2 dex in abundances for S/N > 50, after calibrating Teff to the infrared flux method, validating log g with asteroseismology, correcting [Fe/H] for non-LTE effects, and removing temperature trends in [X/Fe] using wide binaries. [Fe/H] is claimed valid down to about −4, while [X/Fe] are mostly valid for [Fe/H] > −2. The catalog is public.

Load-bearing premise

The catalog is only as good as the training labels: if the APOGEE DR17 and GALAH DR3 abundances are systematically wrong, or if the network invents plausible but wrong labels in regions where the training set is thin (especially [Fe/H] < −2 for elements other than Fe and alpha elements), the catalog inherits those errors.

Editorial extensions

If this is right

  • At S/N > 50 the catalog resolves the high-α and low-α disk sequences, the accreted Gaia-Enceladus-Sausage population in giants, and chemically peculiar dwarfs, so these structures can be mapped in 22-element space rather than in a few elements.
  • The same broad-wavelength low-resolution spectra can deliver nine s- and r-process elements (Sr, Y, Zr, Ba, La, Ce, Nd, Sm, Eu) with 0.1–0.2 dex precision, enabling nucleosynthetic studies on millions of stars rather than thousands.
  • The quoted errors sit close to the Cramér-Rao lower bound for most elements (within a factor of about 1–2), meaning the LAMOST spectra are being used near the information limit for these labels.
  • For stars with [Fe/H] > −2, the catalog is internally homogeneous across the full temperature range after wide-binary calibration, allowing abundance trends to be studied without strong temperature systematics for dwarfs.
  • The catalog provides flags for unreliable metal-poor regimes, so users can isolate where [X/Fe] is not trustworthy; for [Fe/H] the usable range extends to about −4.

Reading between the lines

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

  • A testable extension: retrain the same network after adding high-resolution very-metal-poor samples with measured N, Al, Mn, Si, and heavier elements; if the shaded unreliable regimes in the [X/Fe]–[Fe/H] planes shrink, the current validity floor of −2 for [X/Fe] is set by training data, not by the spectral information content.
  • Because the wide-binary calibration only corrects temperature trends for dwarfs and assumes binary components share initial abundances, residual scatter of 0.1–0.2 dex for heavy elements in binaries is a lower bound on the chemical-tagging precision achievable for dwarf stars; giant-star precision may differ because no equivalent correction was applied.
  • The same pipeline could be applied to other large-area low-resolution surveys; if cross-survey label transfer works, the main limitation for 22-element abundances becomes the training-label accuracy, not spectral resolution, as the Cramér-Rao comparison already suggests.
  • Users who combine this catalog with APOGEE/GALAH should be aware that zero points are tied to A(Fe)=7.45 and the GALAH/APOGEE abundance scales; failing to propagate these zero points would introduce systematics larger than the statistical errors.
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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 / 6 minor

Summary. The paper presents an updated DD-Payne analysis of LAMOST DR9 low-resolution spectra, using training labels from APOGEE DR17, GALAH DR3, and the Li et al. (2022) very metal-poor sample, to derive stellar labels for 6.4 million FGK stars and abundance ratios for 22 elements for about 3.6 million stars with S/N > 20. The authors apply external calibrations: effective temperature to the IRFM scale, surface gravity validated with asteroseismology, [Fe/H] corrected for NLTE effects, and [X/Fe] calibrated internally with wide binaries. The catalog is validated against test sets, PASTEL, star clusters, and Cramer-Rao lower bounds, and is made publicly available.

Significance. If the claims hold, this is a valuable resource for Galactic archaeology: a homogeneous, large-sample catalog with 22 elemental abundances, including several s- and r-process elements, derived from low-resolution spectra. The paper is strong on validation: it includes machine-checkable comparisons against APOGEE, GALAH, PASTEL, asteroseismology, wide binaries, open clusters, and theoretical precision limits, and the catalog is public. The main limitation is that the recommended catalog mixes LTE-based [X/Fe] with an NLTE-corrected [Fe/H] for several elements, which introduces a metallicity-dependent bias that must be addressed before the catalog can be used as advertised.

major comments (3)
  1. [§4.3 and Table 2]
  2. [§5, Abstract, and Fig. 15]
  3. [§4.1, §4.5, and Table 3]
minor comments (6)
  1. [Abstract and §1]
  2. [§4.3]
  3. [§5.2]
  4. [§5.3 and Fig. 23]
  5. [§4.5]
  6. [§4.4]

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DD-Payne catalog is an externally anchored supervised regression; internal calibrations are acknowledged and not predictions by construction.

full rationale

The derivation chain is a supervised spectral-label regression: DD-Payne maps normalized LAMOST spectra to labels using training labels from APOGEE DR17, GALAH DR3, and Li et al. (2022), regularized by Kurucz model gradients. The held-out APOGEE/GALAH test stars are not used in training, so the test-set comparisons in Figs. 9-10 are genuine, although not fully external because both training and test labels come from the same survey systems. The Teff calibration uses IRFM photometry, log g uses asteroseismic nu_max, the [Fe/H] NLTE correction uses the independent Amarsi et al. (2022) grid applied to Li et al. (2022), and the wide-binary calibration is an internal relative-temperature correction, not a claim of absolute prediction; its zero point is anchored to M67. The only in-sample check is the Fig. 15 comparison with Li et al. (2022), which the paper explicitly notes is part of the training set and therefore not an independent validation; this weakens support for the [Fe/H] < -3 regime but does not make the derivation circular, because the DD-Payne output is not algebraically equal to the training labels and the claim also leans on the external PASTEL comparison for higher metallicities. The LTE/NLTE scale mismatch between [Fe/H] and APOGEE-trained [X/Fe] raised in the skeptic attack is an internal-consistency/correctness issue in the catalog columns, not a case of a prediction reducing to its input by construction; no equation in the paper defines [X/Fe] as the NLTE-corrected [Fe/H] or vice versa. No load-bearing self-citation chain or uniqueness theorem is invoked. Hence no significant circularity.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are the calibration coefficients and regularization weights. The main assumptions are the quality and coverage of the training labels, which are the load-bearing parts of the data-driven approach.

free parameters (3)
  • Dscale regularization weights = 10x larger for [X/Fe] than for strong labels
    Empirically determined in Eq. 5 and Sect. 3.1 to balance regularization contributions of weak versus strong spectral features.
  • Cubic temperature-trend coefficients a1, a2, a3 for each element = Derived from MCMC fits to wide binary differential abundances
    Eq. 10 and Sect. 4.5. These coefficients define the temperature-dependent systematic correction applied to the catalog. They are fit to the data, not derived from first principles.
  • Zero-point constant c for each [X/Fe] = Tied to M67 giant abundances
    Sect. 4.5. The absolute zero point of the abundance scale is set by assuming M67 dwarfs and giants share abundances and that M67 giants are on the chosen scale. This is a free calibration choice.
assumptions (6)
  • domain assumption Training labels from APOGEE DR17 and GALAH DR3 are accurate enough to serve as ground truth.
    Used throughout Sect. 3. If these labels have unknown systematics, the DD-Payne predictions inherit them.
  • domain assumption The neural network interpolation is valid in regions of parameter space sparsely covered by the training set.
    Sect. 3.2 extends to very metal-poor stars using only 345 extra stars; Sect. 5 acknowledges limited validity for many elements below [Fe/H] ~ -2.
  • domain assumption The Kurucz ATLAS12/SYNTHE model atmospheres provide accurate gradient spectra for regularization.
    Sect. 3.3. The regularization term in Eq. 5 assumes the physical gradients are correct.
  • domain assumption Wide binary components share identical initial abundances and have not been altered by atomic diffusion or other processes.
    Sect. 4.5. Used to derive the temperature trend calibration.
  • standard math The asteroseismic scaling relation log g = log g_sun + log(nu_max/nu_max_sun) + 0.5 log(Teff/Teff_sun) is valid for the stars used.
    Sect. 4.2. The relation is empirical and generally accepted, but it is not exact for every star.
  • domain assumption NLTE corrections for Fe I from Amarsi et al. (2022) are accurate and can be applied to the Li et al. (2022) LTE abundances.
    Sect. 4.3. The correction is applied per star using median Fe I line corrections.

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Cite this review

Pith. "Pith review of Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne." pith.science (2026). https://pith.science/paper/GHYXBXMT

@misc{pith2026250602763,
  author       = {Pith},
  title        = {Pith review of: Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GHYXBXMT}},
  note         = {Machine review of arXiv:2506.02763}
}
abstract

A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars released in LAMOST DR9, and abundances for 22 elements, namely, C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Ni, Sr, Y, Zr, Ba, La, Ce, Nd, Sm, and Eu, for nearly 3.6 million stars with spectral signal-to-noise ratio (SNR) higher than 20. The [Fe/H] is valid down to $\sim$-4.0, while elemental abundance ratios [X/Fe] are mostly valid for stars with [Fe/H] $\gtrsim-2.0$. Measurement errors in these labels are sensitive to and almost inversely proportional with SNR. For stars with S/N>50, we achieved a typical error of 30 K in Teff, 0.07 dex in $\log g$, $\sim0.05$ dex in abundances for most elements with atomic number smaller than Sr, and 0.1--0.2 dex for heavier elements. Homogenization to the label estimates is carried out via dedicated internal and external calibration. In particular, the non-local thermal equilibrium effect is corrected for the [Fe/H] estimates, the Teff is calibrated to the infrared flux method scale, and the $\log~g$ is validated with asteroseismic measurements. The elemental abundances are internally calibrated using wide binaries, eliminating systematic trend with effective temperature. The catalog is publicly available.

Figures

Figures reproduced from arXiv: 2506.02763 by the authors.

Figure 1
Figure 1. Left: Distribution of the LAMOST DR9 sample stars as a function of g-band S/N , and right: in the (BP −RP, G) color-magnitude diagram. 180∘ 240∘ 300∘ 0∘ 60∘ 120∘ 180∘ l (degree) -75° -60° -45° -30° -15° 0° 15° 30° 45° 60° 75° b (degree) 1 1000 2000 3000 4000 5000 N [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The LAMOST DR9 stellar density distribution in the Galactic coordinate. Each pixel of the plot represents a constant sky area of 4.0 square degrees (2.0 ◦ × 2.0 ◦ ). It combines the advantages of both the flexibility of a data￾driven machine learning approach and the physical inter￾pretability of a model-driven method. Specifically, it trains a data-driven neural network model between the survey spectra and their la… view at source ↗
Figure 3
Figure 3. Stellar density distribution of the LAMOST-APOGEE training sample in the Teff –log g diagram and [X/Fe]-[Fe/H] planes. The labels shown are either from the APOGEE DR17 catalog or from the LAMOST VMP catalog of Li et al. (2022). Some VMP stars have no abundance measurements for N, Al, Mn, Si, and we have assigned constant values to them in the training process. ments for most stars in the GALAH DR3 catalog. As C and … view at source ↗
Figures from the paper (25 more)
Figure 4
Figure 4. Figure 4: Stellar density distribution of the LAMOST-GALAH training samples in the Teff –log g diagram and [X/Fe]-[Fe/H] planes. The top three rows show distributions of the training set 3 as listed in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Loss values as a function of training epoch for the mod￾elling of a particular pixel of spectrum as an example. The grey curve shows the loss value calculated for the spectral flux of the training set, while the blue one shows the regularization term. The black curve s…
Figure 6
Figure 6. Figure 6: The normalized LAMOST spectrum of a particular star as an example. The blue line is for the observed LAMOST spectra, while the red line is the best-fit DD-PAYNE model, which parameters marked on top of the figure. The zoom-in plot shows the spectrum in a small window. …
Figure 7
Figure 7. Figure 7: Comparisons of differential spectrum for Teff , log g, [Fe/H], vmic, and [X/Fe] for C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Ni between the DD-PAYNE prediction (red) and the Kurucz ab initio model calculation (blue) for a fiducial star, with Teff = 5117 K, log g = 3.29, an…
Figure 8
Figure 8. Figure 8: Comparison of differential spectrum for Na, Sr, Y, Zr, Ba, La, Ce, Nd, Sm, and Eu between DD-PAYNE prediction (red) and the Kurucz ab initio model calculation (blue) for the same star as in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Comparisons of the stellar parameters (Teff , log g, [Fe/H], vmic) and elemental abundances between DD-PAYNE determinations and those from APOGEE DR17 catalog for the validation sample. Color represents the number density of stars. Solid lines show the 1:1 line of X an…
Figure 10
Figure 10. Figure 10: Comparison of the stellar parameters (Teff , log g, [Fe/H], vmic) and elemental abundances between DD-PAYNE determinations and those from GALAH DR3 catalog for the validation sample. Solid lines show the 1:1 line of X and Y axes. The mean and dispersion of the differe…
Figure 11
Figure 11. Figure 11: Covariance among different labels for DD-PAYNE spectral fitting. Colors represent the median value of correlation coefficients, derived from the covariance matrix for dwarfs (lef t) and giants (right), separately. The numbers marked in the figure are identical to the …
Figure 12
Figure 12. Figure 12: Comparison of effective temperature between DD-PAYNE determination and the IRFM estimates for giants (left) and dwarfs (right). Color represents the number density of stars. Solid lines in the upper panels show the 1:1 line of X and Y axes. The dotted lines in the bot…
Figure 13
Figure 13. Figure 13: The one-to-one comparison of log g values for 8981 red giants, and 426 subgiant and dwarf stars between the asteroseismic measurements and DD-PAYNE determinations. The red solid line in the upper panel shows the 1:1 line of the X- and Y-axes. The mean and dispersion o…
Figure 14
Figure 14. Figure 14: The amount of NLTE correction for [Fe/H] of the metal￾poor sample stars of Li et al. (2022). Colors represent log g of the stars. veys with lower spectra resolution (e.g. Xiang et al. 2017; Soubiran et al. 2022). The PASTEL catalog includes 31,401 stars with Teff, log…
Figure 15
Figure 15. Figure 15: Comparing the DD-PAYNE [Fe/H] determinations with high-resolution spectroscopy samples from the PASTEL catalog (Soubiran et al. 2016) and the LAMOST VMP catalog of Li et al. (2022). The left panel shows DD-PAYNE [Fe/H] determination using the LAMOST￾APOGEE training se…
Figure 16
Figure 16. Figure 16: Comparison of stellar abundances between the pair components of wide binaries. The grey dots show the abundance determinations before calibration. The blue dots show the abundances after calibration as a function of Teff values. The median and dispersion of the differ…
Figure 17
Figure 17. Figure 17: The differential abundance between the wide binary component stars, ∆[X/Fe], as a function of Teff of the primary. The grey dots show the results for abundances before calibration, while the dots in colors are results for abundances after calibration, and the colors r…
Figure 18
Figure 18. Figure 18: Abundance patterns of solar twins for DD-PAYNE abundances before (grey) and after calibration (red). Solar twins are selected based on the atmospheric parameters using criteria listed in Equation (12). The error bar delineates the median measurement error of the indiv…
Figure 19
Figure 19. Figure 19: Stellar number density distribution in the Teff –log g diagram for the DD-PAYNE results. The left panel is for all the LAMOST DR9 sample stars, while the middle panel is for stars with S/N > 30. The right panel shows the Teff -log g distribution of a randomly selected…
Figure 20
Figure 20. Figure 20: Stellar number density distribution in the Teff –[Fe/H] plane for the DD-PAYNE results. The left and right panels are all sample stars and sample stars with S/N > 30, respectively. 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 [Fe/H] 0.4 0.2 0.0 0.2 0.4 0.6 [Mg/Fe] dwarfs 10 10…
Figure 21
Figure 21. Figure 21: Stellar number density distributions in the [Fe/H]–[Mg/Fe] plane for dwarf stars (left) and for giant stars (right). In both panels only stars with spectral S/N > 30 are shown. to lower metallicity (e.g. Xiang et al. 2024). Finally, stars with [Fe/H] ≲ −2 may be a com…
Figure 22
Figure 22. Figure 22: Stellar number density distributions in the [X/Fe]–[Fe/H] planes for LAMOST giants with S/N > 50. The red dotted line shows the mean trend giant stars in either APOGEE DR17 or GALAH DR3, depending on which is adopted as the source of the training set. For elemental ab…
Figure 23
Figure 23. Figure 23: Stellar number density distributions in the [X/Fe]–[Fe/H] planes for LAMOST dwarfs with S/N > 50. Except for C and N, the abundances refer to those after calibration. The red dotted line shows the mean trend of dwarf stars in either APOGEE DR17 or GALAH DR3, depending…
Figure 24
Figure 24. Figure 24: Comparison of abundance dispersion between member stars of open cluster and field stars with the same metallicity as the cluster. The top and bottom panels show results for M44 (42 dwarfs) and M67 (87 dwarfs), respectively. As marked in the figure, the abundance dispe…
Figure 25
Figure 25. Figure 25: Measurement errors of the DD-PAYNE stellar labels compared to theoretical precision limit given by the CRLB, i.e., Cramer-Rao ´ lower bound. The upper panels show the case for a fiducial dwarf with Teff = 5900 K, log g = 4.0, and [Fe/H]=−0.5, while the lower panels sh…
Figure 26
Figure 26. Figure 26: Comparison of basic atmospheric parameters between DD-PAYNE determinations and the LAMOST DR9 official releases, from left to right are Teff , log g, and [Fe/H], respectively. Color represents the number density of stars. Here only stars with spectral S/N > 30 are sho…
Figure 27
Figure 27. Figure 27: Comparison of stellar parameters and abundances between DD-PAYNE determination in this work and the LAMOST DR5 DD￾PAYNE catalog of Xiang et al. (2019). Acknowledgments We thank the referee for the suggestions that have improved the clarity of the manuscript. This work…
Figure 28
Figure 28. Figure 28: Comparison of s-process elemental abundances between DD-PAYNE determination in this work with Song et al. (2024), who derived abundances for Barium stars with a data-driven approach MEASNet. The hard cut at [Ba/Fe] = 0.3 in the MEASNet estimates corresponds to the def…

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Works this paper leans on

93 extracted references · 7 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]

    M., Liljegren, S., & Nissen, P

    Amarsi, A. M., Liljegren, S., & Nissen, P. E. 2022, A&A, 668, A68, doi: 10.1051/0004-6361/202244542

    doi:10.1051/0004-6361/202244542 2022

  3. [3]

    M., Lind, K., Asplund, M., Barklem, P

    Amarsi, A. M., Lind, K., Asplund, M., Barklem, P. S., & Collet, R. 2016, MNRAS, 463, 1518, doi: 10.1093/mnras/stw2077

    doi:10.1093/mnras/stw2077 2016

  4. [4]

    M., Nissen, P

    Amarsi, A. M., Nissen, P. E., & Sk´ulad´ottir, ´A. 2019, A&A, 630, A104, doi: 10.1051/0004-6361/201936265

    doi:10.1051/0004-6361/201936265 2019

  5. [5]

    M., Lind, K., Osorio, Y ., et al

    Amarsi, A. M., Lind, K., Osorio, Y ., et al. 2020, A&A, 642, A62, doi: 10.1051/0004-6361/202038650

    doi:10.1051/0004-6361/202038650 2020

  6. [6]

    M., & Grevesse, N

    Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, A&A, 653, A141, doi: 10.1051/0004-6361/202140445

    doi:10.1051/0004-6361/202140445 2021

  7. [7]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222

    arXiv 2009

  8. [8]

    2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    Bacon, R., Accardo, M., Adjali, L., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2010

Show all 93 references
  1. [9]

    7735, Ground-based and Airborne Instrumentation for Astronomy III, ed

    Series, V ol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 773508, doi: 10.1117/12.856027

    doi

  2. [10]

    W., Koposov, S

    Belokurov, V ., Erkal, D., Evans, N. W., Koposov, S. E., & Deason, A. J. 2018, MNRAS, 478, 611, doi: 10.1093/mnras/sty982

    2018 doi

  3. [11]

    2022, MNRAS, 514, 689, doi: 10.1093/mnras/stac1267

    Belokurov, V ., & Kravtsov, A. 2022, MNRAS, 514, 689, doi: 10.1093/mnras/stac1267

    2022 doi

  4. [13]

    2014, A&A, 569, A111, doi: 10.1051/0004-6361/201423945 34 Z HANG ET AL

    Blanco-Cuaresma, S., Soubiran, C., Heiter, U., & Jofr´e, P. 2014, A&A, 569, A111, doi: 10.1051/0004-6361/201423945 34 Z HANG ET AL

    2014 doi

  5. [14]

    J., Koch, D., Basri, G., et al

    Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977, doi: 10.1126/science.1185402

    2010 doi

  6. [15]

    2021, MNRAS, 506, 150, doi: 10.1093/mnras/stab1242

    Buder, S., Sharma, S., Kos, J., et al. 2021, MNRAS, 506, 150, doi: 10.1093/mnras/stab1242

    2021 doi

  7. [16]

    X., et al

    Buder, S., Kos, J., Wang, E. X., et al. 2024, arXiv e-prints, arXiv:2409.19858, doi: 10.48550/arXiv.2409.19858

    2024 doi arXiv

  8. [17]

    L., L´epine, S., Newberg, H

    Carlin, J. L., L´epine, S., Newberg, H. J., et al. 2012, Research in Astronomy and Astrophysics, 12, 755, doi: 10.1088/1674-4527/12/7/004

    2012 doi

  9. [18]

    2010, A&A, 512, A54, doi: 10.1051/0004-6361/200913204

    Casagrande, L., Ram´ırez, I., Mel´endez, J., Bessell, M., & Asplund, M. 2010, A&A, 512, A54, doi: 10.1051/0004-6361/200913204

    2010 doi

  10. [19]

    2019, The Messenger, 175, 30, doi: 10.18727/0722-6691/5122

    Chiappini, C., Minchev, I., Starkenburg, E., et al. 2019, The Messenger, 175, 30, doi: 10.18727/0722-6691/5122

    2019 doi

  11. [20]

    2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    2016 doi

  12. [21]

    1946, Mathematical Methods of Statistics, Princeton Mathematical Series No

    Cramer, H. 1946, Mathematical Methods of Statistics, Princeton Mathematical Series No. 9 (Princeton: Princeton University Press)

    1946

  13. [22]

    C., et al

    Dalton, G., Trager, S., Abrams, D. C., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2014

  14. [23]

    9147, Ground-based and Airborne Instrumentation for Astronomy V , ed

    Series, V ol. 9147, Ground-based and Airborne Instrumentation for Astronomy V , ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91470L, doi: 10.1117/12.2055132 De Cat, P., Fu, J. N., Ren, A. B., et al. 2015, ApJS, 220, 19, doi: 10.1088/0067-0049/220/1/19 deJong, R. S., Agertz, O....

    2015 doi

  15. [24]

    J., Liu, C., et al

    Deng, L.-C., Newberg, H. J., Liu, C., et al. 2012, Research in Astronomy and Astrophysics, 12, 735, doi: 10.1088/1674-4527/12/7/003 DESI Collaboration, Aghamousa, A., Aguilar, J., et al. 2016, arXiv e-prints, arXiv:1611.00036, doi: 10.48550/arXiv.1611.00036

    2012 doi arXiv

  16. [25]

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

    2021 doi

  17. [26]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  18. [27]

    D., Zong, W., et al

    Fu, J.-N., Cat, P. D., Zong, W., et al. 2020, Research in Astronomy and Astrophysics, 20, 167, doi: 10.1088/1674-4527/20/10/167 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2021, A&A, 649, A1, doi: 10.1051/0004-6361/202039657

    2020 doi

  19. [28]

    M., et al

    Gao, X., Lind, K., Amarsi, A. M., et al. 2018, MNRAS, 481, 2666, doi: 10.1093/mnras/sty2414 Garcia P´erez, A. E., Allende Prieto, C., Holtzman, J. A., et al. 2016, AJ, 151, 144, doi: 10.3847/0004-6256/151/6/144

    2018 doi

  20. [29]

    C., et al

    Gilmore, G., Randich, S., Worley, C. C., et al. 2022, A&A, 666, A120, doi: 10.1051/0004-6361/202243134 Gonz´alez Hern´andez, J. I., & Bonifacio, P. 2009, A&A, 497, 497, doi: 10.1051/0004-6361/200810904

    2022 doi

  21. [30]

    Grevesse, N., Asplund, M., & Sauval, A. J. 2007, SSRv, 130, 105, doi: 10.1007/s11214-007-9173-7

    2007 doi

  22. [31]

    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

  23. [32]

    H., et al

    Helmi, A., Babusiaux, C., Koppelman, H. H., et al. 2018, Nature, 563, 85, doi: 10.1038/s41586-018-0625-x

    2018 doi

  24. [33]

    2014, Contributions of the Astronomical Observatory Skalnate Pleso, 43, 518

    Henden, A., & Munari, U. 2014, Contributions of the Astronomical Observatory Skalnate Pleso, 43, 518

    2014

  25. [34]

    A., Templeton, M., Terrell, D., et al

    Henden, A. A., Templeton, M., Terrell, D., et al. 2016, VizieR Online Data Catalog: AA VSO Photometric All Sky Survey (APASS) DR9 (Henden+, 2016), VizieR On-line Data Catalog: II/336. Originally published in: 2015AAS...22533616H

    2016

  26. [35]

    Turnbull, M. C. 2014, AJ, 148, 54, doi: 10.1088/0004-6256/148/3/54

    2014 doi

  27. [36]

    C., et al

    Hourihane, A., Franc ¸ois, P., Worley, C. C., et al. 2023, A&A, 676, A129, doi: 10.1051/0004-6361/202345910

    2023 doi

  28. [37]

    R., Stello, D., et al

    Huber, D., Bedding, T. R., Stello, D., et al. 2011, ApJ, 743, 143, doi: 10.1088/0004-637X/743/2/143 Jofr´e, P., Heiter, U., Soubiran, C., et al. 2014, A&A, 564, A133, doi: 10.1051/0004-6361/201322440

    2011 doi

  29. [38]

    G., Larsson, M., Iwamae, A., & Yu, B

    Jorgensen, U. G., Larsson, M., Iwamae, A., & Yu, B. 1996, A&A, 315, 204

    1996

  30. [39]

    Keenan, P. C. 1954, ApJ, 120, 484, doi: 10.1086/145937

    1954 doi

  31. [40]

    P., & Ba, J

    Kingma, D. P., & Ba, J. 2014, arXiv e-prints, arXiv:1412.6980, doi: 10.48550/arXiv.1412.6980

    2014 doi arXiv

  32. [41]

    Kjeldsen, H., & Bedding, T. R. 1995, A&A, 293, 87, doi: 10.48550/arXiv.astro-ph/9403015

    1995 doi arXiv

  33. [42]

    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

  34. [43]

    J., Grundahl, F., Richard, O., et al

    Korn, A. J., Grundahl, F., Richard, O., et al. 2007, ApJ, 671, 402, doi: 10.1086/523098

    2007 doi

  35. [44]

    Kurucz, R. L. 1970, SAO Special Report, 309 —. 1993, SYNTHE spectrum synthesis programs and line data —. 2005, Memorie della Societa Astronomica Italiana Supplementi, 8, 14 —. 2017, Canadian Journal of Physics, 95, 825, doi: 10.1139/cjp-2016-0794

    1970 doi

  36. [45]

    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

    2022 doi

  37. [46]

    Lind, K., & Amarsi, A. M. 2024, ARA&A, 62, 475, doi: 10.1146/annurev-astro-052722-103557

    2024 doi

  38. [47]

    2012, MNRAS, 427, 50, doi: 10.1111/j.1365-2966.2012.21686.x

    Lind, K., Bergemann, M., & Asplund, M. 2012, MNRAS, 427, 50, doi: 10.1111/j.1365-2966.2012.21686.x

    2012

  39. [48]

    2021, A&A, 649, A4, doi: 10.1051/0004-6361/202039653

    Lindegren, L., Bastian, U., Biermann, M., et al. 2021, A&A, 649, A4, doi: 10.1051/0004-6361/202039653

    2021 doi

  40. [49]

    2020, arXiv e-prints, arXiv:2005.07210, doi: 10.48550/arXiv.2005.07210

    Liu, C., Fu, J., Shi, J., et al. 2020, arXiv e-prints, arXiv:2005.07210, doi: 10.48550/arXiv.2005.07210

    2020 doi arXiv

  41. [50]

    W., Yuan, H

    Liu, X. W., Yuan, H. B., Huo, Z. Y ., et al. 2014, in Setting the scene for Gaia and LAMOST, ed. S. Feltzing, G. Zhao, N. A. Walton, & P. Whitelock, V ol. 298, 310–321, doi: 10.1017/S1743921313006510 SAMPLE ARTICLE 35

    2014 doi

  42. [51]

    L., Zhao, Y .-H., Zhao, G., et al

    Luo, A. L., Zhao, Y .-H., Zhao, G., et al. 2015, Research in Astronomy and Astrophysics, 15, 1095, doi: 10.1088/1674-4527/15/8/002

    2015 doi

  43. [52]

    R., Schiavon, R

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

    2017 doi

  44. [53]

    R., Korn, A

    Mashonkina, L., Gehren, T., Shi, J. R., Korn, A. J., & Grupp, F. 2011, A&A, 528, A87, doi: 10.1051/0004-6361/201015336

    2011 doi

  45. [54]

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

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

    2015 doi

  46. [55]

    A., Breton, S., et al

    Mathur, S., Garc´ıa, R. A., Breton, S., et al. 2022, A&A, 657, A31, doi: 10.1051/0004-6361/202141168

    2022 doi

  47. [56]

    2015, Atomic Diffusion in Stars, doi: 10.1007/978-3-319-19854-5

    Michaud, G., Alecian, G., & Richer, J. 2015, Atomic Diffusion in Stars, doi: 10.1007/978-3-319-19854-5

    2015 doi

  48. [57]

    Koposov, S. E. 2018, ApJL, 863, L28, doi: 10.3847/2041-8213/aad7f7

    2018 doi

  49. [58]

    W., Rix, H

    Ness, M., Hogg, D. W., Rix, H. W., Ho, A. Y . Q., & Zasowski, G. 2015, ApJ, 808, 16, doi: 10.1088/0004-637X/808/1/16

    2015 doi

  50. [59]

    J., Casey, A

    Norfolk, B. J., Casey, A. R., Karakas, A. I., et al. 2019, MNRAS, 490, 2219, doi: 10.1093/mnras/stz2630 O’Briain, T., Ting, Y .-S., Fabbro, S., et al. 2021, ApJ, 906, 130, doi: 10.3847/1538-4357/abca96

    2019 doi

  51. [60]

    Rao, C. R. 1945, Bulletin of the Calcutta Mathematical Society, 37, 81

    1945

  52. [61]

    2016, A&A, 585, A93, doi: 10.1051/0004-6361/201425030

    Recio-Blanco, A., de Laverny, P., Allende Prieto, C., et al. 2016, A&A, 585, A93, doi: 10.1051/0004-6361/201425030

    2016 doi

  53. [62]

    A., et al

    Recio-Blanco, A., de Laverny, P., Palicio, P. A., et al. 2023, A&A, 674, A29, doi: 10.1051/0004-6361/202243750

    2023 doi

  54. [63]

    2016, Research in Astronomy and Astrophysics, 16, 45, doi: 10.1088/1674-4527/16/3/045

    Ren, J.-J., Liu, X.-W., Xiang, M.-S., et al. 2016, Research in Astronomy and Astrophysics, 16, 45, doi: 10.1088/1674-4527/16/3/045

    2016 doi

  55. [64]

    2022, ApJ, 941, 45, doi: 10.3847/1538-4357/ac9e01

    Rix, H.-W., Chandra, V ., Andrae, R., et al. 2022, ApJ, 941, 45, doi: 10.3847/1538-4357/ac9e01

    2022 doi

  56. [65]

    R., Weisz, D

    Sandford, N. R., Weisz, D. R., & Ting, Y .-S. 2020, ApJS, 249, 24, doi: 10.3847/1538-4365/ab9cb0

    2020 doi

  57. [66]

    2020, A&A, 643, A164, doi: 10.1051/0004-6361/202038833

    Semenova, E., Bergemann, M., Deal, M., et al. 2020, A&A, 643, A164, doi: 10.1051/0004-6361/202038833

    2020 doi

  58. [67]

    2015, ApJ, 808, 148, doi: 10.1088/0004-637X/808/2/148

    Sitnova, T., Zhao, G., Mashonkina, L., et al. 2015, ApJ, 808, 148, doi: 10.1088/0004-637X/808/2/148

    2015 doi

  59. [68]

    F., Cutri, R

    Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708

    2006 doi

  60. [69]

    V ., & Lambert, D

    Smith, V . V ., & Lambert, D. L. 1990, ApJS, 72, 387, doi: 10.1086/191421

    1990 doi

  61. [70]

    2024, ApJ, 974, 78, doi: 10.3847/1538-4357/ad6b2c

    Song, S., Kong, X., Bu, Y ., Yi, Z., & Liu, M. 2024, ApJ, 974, 78, doi: 10.3847/1538-4357/ad6b2c

    2024 doi

  62. [71]

    2022, A&A, 663, A4, doi: 10.1051/0004-6361/202142409

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

    2022 doi

  63. [72]

    2016, A&A, 591, A118, doi: 10.1051/0004-6361/201628497

    Soubiran, C., Le Campion, J.-F., Brouillet, N., & Chemin, L. 2016, A&A, 591, A118, doi: 10.1051/0004-6361/201628497

    2016 doi

  64. [73]

    L., Lagarde, N., et al

    Soubiran, C., Creevey, O. L., Lagarde, N., et al. 2024, A&A, 682, A145, doi: 10.1051/0004-6361/202347136

    2024 doi

  65. [74]

    V ., et al

    Souto, D., Cunha, K., Smith, V . V ., et al. 2018, ApJ, 857, 14, doi: 10.3847/1538-4357/aab612

    2018 doi

  66. [75]

    2019, ApJ, 874, 97, doi: 10.3847/1538-4357/ab0b43

    Souto, D., Allende Prieto, C., Cunha, K., et al. 2019, ApJ, 874, 97, doi: 10.3847/1538-4357/ab0b43

    2019 doi

  67. [76]

    2017a, ApJ, 843, 32, doi: 10.3847/1538-4357/aa7688 —

    Ting, Y .-S., Conroy, C., Rix, H.-W., & Cargile, P. 2017a, ApJ, 843, 32, doi: 10.3847/1538-4357/aa7688 —. 2019, ApJ, 879, 69, doi: 10.3847/1538-4357/ab2331

    2019 doi

  68. [77]

    Ting, Y .-S., Rix, H.-W., Conroy, C., Ho, A. Y . Q., & Lin, J. 2017b, ApJL, 849, L9, doi: 10.3847/2041-8213/aa921c

    doi

  69. [78]

    L., Chen, J.-J., et al

    Wang, R., Luo, A. L., Chen, J.-J., et al. 2020, ApJ, 891, 23, doi: 10.3847/1538-4357/ab6dea

    2020 doi

  70. [79]

    R., Sharma, S., et al

    Wang, Z., Hayden, M. R., Sharma, S., et al. 2022, MNRAS, 514, 1034, doi: 10.1093/mnras/stac1425

    2022 doi

  71. [80]

    2014, in Statistical Challenges in 21st Century Cosmology, ed

    Wu, Y ., Du, B., Luo, A., Zhao, Y ., & Yuan, H. 2014, in Statistical Challenges in 21st Century Cosmology, ed. A. Heavens, J.-L. Starck, & A. Krone-Martins, V ol. 306, 340–342, doi: 10.1017/S1743921314010825

    2014 doi

  72. [81]

    L., Li, H.-N., et al

    Wu, Y ., Luo, A. L., Li, H.-N., et al. 2011, Research in Astronomy and Astrophysics, 11, 924, doi: 10.1088/1674-4527/11/8/006

    2011 doi

  73. [82]

    2021, ApJS, 253, 22, doi: 10.3847/1538-4365/abd6ba

    Xiang, M., Rix, H.-W., Ting, Y .-S., et al. 2021, ApJS, 253, 22, doi: 10.3847/1538-4365/abd6ba

    2021 doi

  74. [83]

    2024, arXiv e-prints, arXiv:2410.09705, doi: 10.48550/arXiv.2410.09705

    Xiang, M., Rix, H.-W., Yang, H., et al. 2024, arXiv e-prints, arXiv:2410.09705, doi: 10.48550/arXiv.2410.09705

    2024 doi arXiv

  75. [84]

    2019, ApJS, 245, 34, doi: 10.3847/1538-4365/ab5364

    Xiang, M., Ting, Y .-S., Rix, H.-W., et al. 2019, ApJS, 245, 34, doi: 10.3847/1538-4365/ab5364

    2019 doi

  76. [85]

    2022, A&A, 662, A66, doi: 10.1051/0004-6361/202141570

    Xiang, M., Rix, H.-W., Ting, Y .-S., et al. 2022, A&A, 662, A66, doi: 10.1051/0004-6361/202141570

    2022 doi

  77. [86]

    S., Liu, X

    Xiang, M. S., Liu, X. W., Shi, J. R., et al. 2017, MNRAS, 464, 3657, doi: 10.1093/mnras/stw2523

    2017 doi

  78. [87]

    2020, ApJ, 898, 28, doi: 10.3847/1538-4357/ab99a5

    Xiang, M.-S., Rix, H.-W., Ting, Y .-S., et al. 2020, ApJ, 898, 28, doi: 10.3847/1538-4357/ab99a5

    2020 doi

  79. [88]

    2022, The Innovation, 3, 100224, doi: 10.1016/j.xinn.2022.100224

    Yan, H., Li, H., Wang, S., et al. 2022, The Innovation, 3, 100224, doi: 10.1016/j.xinn.2022.100224

    2022

  80. [89]

    R., & Stello, D

    Yu, J., Huber, D., Bedding, T. R., & Stello, D. 2018, MNRAS, 480, L48, doi: 10.1093/mnrasl/sly123

    2018 doi

  81. [90]

    2015, ApJ, 799, 133, doi: 10.1088/0004-637X/799/2/133

    Yuan, H., Liu, X., Xiang, M., et al. 2015, ApJ, 799, 133, doi: 10.1088/0004-637X/799/2/133

    2015 doi

  82. [91]

    2023, ApJ, 946, 110, doi: 10.3847/1538-4357/acbcc4

    Zhang, M., Xiang, M., Zhang, H.-W., et al. 2023, ApJ, 946, 110, doi: 10.3847/1538-4357/acbcc4

    2023 doi

  83. [92]

    2024, arXiv e-prints, arXiv:2402.06242, doi: 10.48550/arXiv.2402.06242

    Zhang, M., Xiang, M., Ting, Y .-S., et al. 2024, arXiv e-prints, arXiv:2402.06242, doi: 10.48550/arXiv.2402.06242

    2024 doi arXiv

  84. [93]

    2012, Research in Astronomy and Astrophysics, 12, 723, doi: 10.1088/1674-4527/12/7/002

    Zhao, G., Zhao, Y .-H., Chu, Y .-Q., Jing, Y .-P., & Deng, L.-C. 2012, Research in Astronomy and Astrophysics, 12, 723, doi: 10.1088/1674-4527/12/7/002

    2012 doi

  85. [94]

    2020, ApJS, 251, 15, doi: 10.3847/1538-4365/abbb2d

    Zong, W., Fu, J.-N., De Cat, P., et al. 2020, ApJS, 251, 15, doi: 10.3847/1538-4365/abbb2d

    2020 doi

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