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Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read DD–Payne, a neural-network interpolator regularized by theoretical gradient spectra, labels 6 million LAMOST stars with parameters and 16-element abundances.

desk verdict A serious and useful catalog paper: 6 million LAMOST stars with 16 abundances, carefully validated for precision, but the "physical abundance" claim leans on a partly circular gradient check. read the letter →

arxiv 1908.09727 v1 pith:VGRLWH5R submitted 2019-08-26 astro-ph.SR astro-ph.GAastro-ph.IM

Maosheng Xiang , Yuan-Sen Ting , Hans-Walter Rix , Nathan Sandford , Sven Buder , Karin Lind , Xiao-Wei Liu , Jian-Rong Shi
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Hua-Wei Zhang
This is my paper · ORCID
classification astro-ph.SRastro-ph.GAastro-ph.IM
keywords LAMOSTDR5stellarabundancesparameterslow-resolutionspectroscopydata-drivenspectralmodelingneuralnetworkinterpolatorgradient-spectrumregularizationGalacticarchaeology
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that the chemical composition of a star, not just its temperature and gravity, can be read from spectra of very low resolution ($R\simeq1800$): a hybrid model the authors call DD–Payne derives parameters and abundances of 16 elements for roughly 6 million stars in the LAMOST DR5 survey. The claim matters because precision abundance work has been assumed to require high-resolution spectroscopy; if it holds, large parts of the Milky Way's chemical map can be built from cheap, wide-field low-resolution surveys. The paper's design problem is that a purely data-driven model may learn to 'predict' abundances from correlations among labels rather than from real spectral lines. DD–Payne answers this by training a neural-network spectral interpolator on LAMOST spectra of stars whose labels come from the GALAH and APOGEE high-resolution surveys, while penalizing any deviation of the network's flux gradients from ab initio Kurucz model gradients. The result is validated by cross-validation and repeat observations, with internal abundance precision of 0.03–0.1 dex for most of the 16 elements at $S/N \ge 50$, and with known systematics traced to the training surveys rather than to the method.

What carries the argument

The central object is the DD–Payne model: a two-hidden-layer neural-network spectral interpolator, inherited from The Payne, that maps a ~20-dimensional label vector onto normalized flux, trained with a loss that joins a data-driven term — fitting LAMOST spectra whose labels come from GALAH DR2 and APOGEE DR14 — to a physics term that penalizes the absolute difference between the network's gradient spectra and the Kurucz ab initio gradient spectra at sixteen fiducial stars. The gradient term is the piece that does the work: it biases the network toward associating each element's abundance with the spectral features that theory says respond to it, and the per-star correlation between empirical and theoretical gradients (with a 0.5 threshold) is what defines which abundance estimates are flagged as physically determined rather than correlation-driven. The same machinery produces the covariance diagnostics, the quality flags, and the uncertainty scaling from repeat observations.

What would settle it

Take benchmark stars with abundances determined independently of the two training surveys, for instance open-cluster members or stars analyzed with 3D/NLTE model atmospheres, and run their LAMOST spectra through the public DD–Payne catalog or model; the central claim fails if the scatter or offsets in the strong-feature elements (Mg, Si, Ca, Ti, Fe, Ni) exceed the claimed 0.03–0.1 dex precision for stars flagged 'reliable', since the gradient-correlation flag is supposed to certify that exactly those features carried the measurement.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that low-resolution ($R\approx1800$) optical spectra carry enough element-by-element information for ~6 million stars to be labeled with 16 abundances (C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Ba) plus $T_{\rm eff}$, $\log g$, and micro-turbulence, provided the data-driven model is physically anchored. The anchor is the loss function (Eq. 2): the network is trained on observed spectra with high-resolution survey labels, and simultaneously forced to reproduce the flux-response spectra $\partial f(\lambda)/\partial l$ of the Kurucz models at 16 fiducial reference stars spanning 4000–7000 K and $[\mathrm{Fe/H}]$ from $-2.5$ to $0.5$. The paper demonstrates the mechanism works by comparing empirical and theoretical gradient spectra across the $T_{\rm eff}$–$[\mathrm{Fe/H}]$ plane: for most elements the correlation is high over most of the plane, while for Li, Sc, V, Zn, Y, and Eu it is not, and those elements are dropped rather than reported. It further shows that the difference between GALAH-trained and APOGEE-trained versions of the catalog reproduces the known GALAH-versus-APOGEE label offsets, so the ~0.1 dex systematics the catalog carries are inherited from the training labels, not created by the model.

Load-bearing premise

The load-bearing premise is that the Kurucz model gradient spectra accurately represent how real stellar flux responds to a change in each element's abundance; the paper itself notes that the theory gradients for $T_{\rm eff}$ and $\log g$ are unreliable enough near parameter-space boundaries to bias those estimates, so wherever the theoretical gradients are wrong, the 'physical' abundances are inherited theory, not measured fact.

Editorial extensions

If this is right

  • A public catalog of ~6 million stars with $T_{\rm eff}$, $\log g$, $V_{\rm mic}$, $[\mathrm{Fe/H}]$, and 16 $[\mathrm{X/Fe}]$ ratios becomes available, roughly an order of magnitude larger than any high-resolution abundance survey, so element-by-element searches can be run on a truly large sample.
  • With gradient-correlation flags applied, 4.26 million stars have physically determined abundances for at least 10 elements, meaning abundance science is possible in parameter regimes where purely data-driven estimates would be suspect.
  • Because the two training surveys disagree at the ~0.1 dex level for elements such as Fe, Mg, Mn, and Ni, the recommended catalog specifies per element whether the GALAH-trained or APOGEE-trained value is adopted, letting users match the abundance scale to their science case.
  • The catalog reproduces the expected thin-disk and thick-disk sequences in the $[\mathrm{Fe/H}]$–$[\alpha/\mathrm{Fe}]$ plane, indicating that abundance ratios from the catalog trace real stellar populations rather than the label correlations the gradient prior was designed to suppress.

Reading between the lines

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

  • The gradient-correlation diagnostic generalizes: any future data-driven spectral model could report, per element per star, how strongly its inferred abundance response matches a theoretical expectation, making 'physically measured versus statistically inferred' an explicit, auditable quantity rather than a design claim.
  • Because systematics are inherited from the training surveys, the catalog is improvable without touching a single LAMOST spectrum: when GALAH or APOGEE re-derive their labels with better line lists or non-LTE corrections, retraining the network propagates the improvement to all 6 million stars.
  • The paper does not run a cluster-based validation; stars in a coeval open cluster share initial chemistry, so cluster abundance scatter should match the claimed internal precision, which makes cluster members a natural independent check of the 0.03–0.1 dex claims.
  • The 16-element bound is a property of the current training labels, not a hard limit of LAMOST spectra: with deeper high-resolution training data, elements excluded here (Li, Zn, Y, Eu) could cross the 0.5 gradient-correlation threshold at high $S/N$.
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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 the Data-Driven Payne (DD-Payne), a hybrid spectral modeling approach that combines The Payne's neural-network spectral interpolation with The Cannon's data-driven training strategy, regularized by theoretical Kurucz gradient spectra. The method is applied to about 8 million LAMOST DR5 low-resolution (R~1800) spectra, yielding stellar parameters (Teff, logg, Vmic) and [X/Fe] for 16 elements for about 6 million unique stars. Training labels come from GALAH DR2 and the Ting et al. (2019) APOGEE-Payne catalog for stars in common with LAMOST; the loss function (Eq. 2) adds a penalty that drives the network's label-gradient spectra toward ab initio Kurucz gradients at 16 reference stars. The results are validated via cross-validation on held-out stars from both surveys, repeat observations (about a quarter of the sample), recovery of the high-alpha sequence and literature abundance trends, and a gradient-correlation flag (Section 5.4.2) intended to certify that abundances are measured from element-specific spectral features rather than astrophysical correlations. The catalog provides per-star uncertainties (scaled from formal fitting errors to repeat-observation scatter), quality flags, and binary/multiple-star tags, and is publicly available.

Significance. If the catalog's accuracy matches its internal precision, this is a landmark data product for Galactic archaeology: it is an order of magnitude larger than any high-resolution abundance sample and demonstrates that multi-element chemical cartography is feasible at R~1800, with direct implications for DESI, WEAVE, and 4MOST. The paper's strengths are substantial: internal precision claims are grounded in a large repeat-observation sample with explicit S/N dependence; cross-validation is performed on held-out stars independent of training; the inheritance of systematic errors from the training sets is not only admitted but quantified by direct GALAH versus APOGEE-Payne comparisons (Appendix C); and the public catalog ships with per-star uncertainties and flags that let users re-cut the sample. The recovery of the well-known high-alpha sequence in the [Fe/H]-[alpha/Fe] diagram is a falsifiable external check that the method passes.

major comments (3)
  1. [Sec. 5.4.2, Eq. (2), Table 1, Figs. 1-2] The 'physical determination' validation of Section 5.4.2 and Figs. 1-2 is partially circular. The DD-Payne gradient spectra are compared against Kurucz gradient spectra of the same 16 reference stars in Table 1 that act as the regularization targets in the loss function (Eq. 2), evaluated with the same step sizes, LSF convolution, and 50 Angstrom normalization, so the check largely certifies that the network learned the imposed prior rather than that the Kurucz gradients are correct. The data term in Eq. (2) prevents full circularity and the gradient agreement is a necessary condition, but a systematic error common to the Kurucz model and the prior (1D/LTE assumptions, line-list incompleteness, or normalization artifacts) would be inherited by the network and still receive flag=1. The paper itself shows that such imprinting occurs: strong Teff/logg gradient priors produce boundary biases (Section 2, Fig. 5), and the [Co/Fe] dwarf trend, opposite to high-resolution literature (Section 5.2), passes the flag system despite being systematically wrong. Additionally, the distance metric used to pick the closest reference star (Section 2) includes only Teff, logg, and [Fe/H], while all 16 reference stars have [X/Fe]=0 (Table 1); the effect of comparing gradients at different points in abundance space is not discussed. I recommend an independent check, for instance gradient agreement against a different model grid (MARCS or PHOENIX) for the same reference stars, or an injection-recovery test using synthetic spectra from a different model, or an explicit statement that flag=1 means consistency with the Kurucz prior rather than certified accuracy.
  2. [Secs. 4.2, 5.2, Tables 2 and 4] The headline claim of abundances for 16 elements is stronger than the evidence for at least three of them. The paper reports that [Co/Fe] for dwarfs shows an opposite trend to literature, 'likely a consequence of the lack of good Co abundance for our training sets' (Section 5.2), consistent with Table 2 showing that only 136 of 4,557 GALAH training stars have flag=0 for Co; [Cu/Fe] and [Ba/Fe] have internal precision of only 0.2-0.3 dex (Section 4.3), and the cross-validation scatter for [O/Fe] and [Ba/Fe] is larger than 0.2 dex (Section 4.2). The abstract acknowledges the Cu and Ba precision caveat but not the Co problem, and the recommended catalog (Table 4) still lists [Co/Fe] from the GALAH-based set with flag=1 for many stars. Coverage claims are also optimistic at the metal-poor end: for the GALAH-trained elements, the underlying model shows Teff/logg/[Fe/H] biases of up to 200 K, 0.5 dex, and 0.2 dex at [Fe/H] < -0.7 (Fig. 6, left), and stars below [Fe/H] ~ -1.5 are extrapolations (Section 3.2), yet Section 5.2 states that metal-poor [Fe/H] estimates are 'reliable, at least for selecting metal-poor star candidates.' The quality flags mitigate these problems and the authors are transparent about them in the body, but the abstract and title should either claim a realistically qualified element set or carry the caveats for Co, Cu, and Ba explicitly.
  3. [Secs. 4.4, 5.3, Table 3, Fig. 12] The per-star uncertainties delivered in the catalog are internal precision only, and for several elements the demonstrated systematics rival or exceed the quoted internal errors. Fig. 12 shows median differences of 0.04-0.08 dex in [Fe/H], 0.09 dex in [Mg/Fe] for dwarfs, and 0.1-0.2 dex in [Mn/Fe] and [Ni/Fe] between the GALAH- and APOGEE-based DD-Payne results, while the internal precision for those elements is 0.03-0.1 dex (Section 4.3); the abstract does mention ~0.1 dex inherited systematics, but the 'err' columns of the public catalog (Table 3) will in practice be read as total uncertainties. In addition, the recommended catalog mixes abundance scales: [Fe/H] is taken from the APOGEE-based training set, while [X/Fe] for nine elements comes from the GALAH-based set, where [X/Fe] is defined relative to the GALAH-based [Fe/H]; this introduces a 0.04-0.08 dex inconsistency in the denominator of the recommended ratios (relative to Fig. 12), and it is not stated in Section 5.1 which [Fe/H] scale each recommended [X/Fe] refers to. I recommend adding per-element systematic error entries or an explicit pointer to Section 4.4 in the catalog documentation, and clarifying the [Fe/H] reference scale of each recommended [X/Fe], for example by publishing [X/H] alongside [X/Fe].
minor comments (6)
  1. [Abstract and Introduction] The phrases 'TheData –DrivenPayne' and 'TheData –DrivenPayne ($DD$–Payne)' have broken spacing and should read 'The Data-Driven Payne (DD-Payne)'.
  2. [Eq. (2)] The gradient notation f' is defined only in prose; the regularization term should state explicitly that the summation runs over wavelength pixels as well as over the Nr reference stars and Nl labels, and that f' denotes the derivative of the model flux with respect to each label evaluated at the reference labels.
  3. [Secs. 2 and 5.4.1] The values of Dscale (5 versus 50), the correlation threshold of 0.5, and the chi2ratio thresholds are admittedly empirical; a brief sensitivity test demonstrating that the catalog labels and flag statistics are stable under moderate changes of these thresholds would strengthen the flag definitions.
  4. [Sec. 4.3 and Figs. 9-11 captions] The text defines the internal precision as the dispersion of pairwise differences divided by sqrt(2), while the captions call it the 'rms standard deviation of the repeat observations'; the two statements are consistent only if the pairwise nature of the estimator is stated in both places.
  5. [Fig. 2 and Sec. 5.4.2] The bin size of the Teff-[Fe/H] grid used for the median correlation maps and the flag assignment is not specified; since the flags are assigned per bin, the bin dimensions should be stated in the text or caption.
  6. [Sec. 3.2 and references] The citation 'Ting et al. (2019)' is used for both The Payne method paper and the APOGEE-Payne catalog, and the text switches between these two uses without a consistently distinguishing label, which is confusing on first reading; also, the reference to Casey et al. (2016) gives only an arXiv number and should be updated to the published version if one exists.

Circularity Check

1 steps flagged · score 4.0 of 10

Physicality certification is partly circular: the Kurucz gradient spectra are both the training regularizer (Eq. 2) and the validation reference (Sec. 5.4.2), so the gradient-agreement flags largely confirm that the optimizer met its own prior, not that the prior is independently correct.

  1. fitted input called prediction [Section 2 (Eq. 2 and Fig. 1 caption) and Section 5.4.2 (X gradcorr flags)]
    "The regularization term is the absolute difference of gradient spectra ∂f(λ)/∂l between the data-driven model f′ and the ab initio Kurucz model f′_ab initio for a number of reference stars ... The results show that, for this reference stellar label, the DD–Payne reproduces the Kurucz model gradient spectra very well, demonstrating that the DD–Payne measures stellar labels (in particular elemental abundances) from ab initio features, instead of drawing from astrophysical correlations among stellar labels."

    The Kurucz gradient spectra appear twice: as the second term of the loss function in Eq. (2), which is minimized during training, and as the reference against which the DD-Payne gradient spectra are correlated in the Section 5.4.2 flag and in Fig. 2. For the Table 1 reference labels, a high correlation is enforced by the loss itself; the Fig. 1 example is essentially one of those fiducial labels. Thus the 'physicality' agreement at the reference points is an in-sample check that optimization worked, not an independent test that the abundances are measured from correct ab initio physics.

full rationale

The main DD-Payne derivation is not circular in the narrow sense: the catalog values for 6 million stars come from a neural network trained on LAMOST spectra with GALAH/APOGEE labels and a Kurucz gradient regularizer, and the precision claims are supported by held-out cross-validation (stars not used in training) and by repeat LAMOST observations, both of which are standard and independent checks. The one clear partial circularity is the physicality certification: the same Kurucz gradient spectra enter as the regularization target in Eq. (2) and as the reference in the gradient-correlation flags (Section 5.4.2). High correlation at the reference labels is therefore partly a statement about how well the optimizer satisfied its own prior, not about the independent correctness of the Kurucz gradients. The paper explicitly acknowledges this conditioning and also provides external anchors (comparison between GALAH- and APOGEE-trained results, literature abundance trends, and direct GALAH-vs-APOGEE-Payne comparisons in the Appendix), so the central abundance catalog retains substantial independent content. Overall, the circularity is real but partial and confined mostly to the 'physically measured vs. correlation-inferred' claim, warranting a score of 4 rather than a higher score.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the accuracy of external training labels, the fidelity of Kurucz model gradients, and the generalization of the neural network interpolator. The free parameters are hyperparameters of the training procedure and quality-flag thresholds, all chosen empirically and documented in the text.

free parameters (7)
  • Dscale = 5 for Teff, logg, Vmic; 50 for [Fe/H] and [X/Fe]
    Strength of the gradient-spectrum prior in the loss function (Eq. 2). Chosen empirically; strong constraints on Teff/logg caused bias at parameter boundaries (Section 2).
  • Gradient evaluation step sizes = 200 K in Teff, 0.25 or 0.5 dex in logg/[Fe/H]/[X/Fe], 1.0 km/s in Vmic
    Numerical step sizes used to compute Kurucz gradient spectra for the fiducial stars (Table 1).
  • Correlation coefficient threshold = 0.5
    Critical value of gradient-spectra correlation for assigning X flag = 1 as physically determined (Section 5.4.2).
  • qflag chi2 thresholds = chi2ratio > 5 for S/N<200, up to >15 for S/N>500
    Arbitrary criterion to mark poor-quality spectral fits (Section 5.4.1).
  • Pixel mask threshold = 0.05 flux difference
    Threshold for masking poorly-trained pixels based on solar spectrum comparison (Section 4.1).
  • Normalization smoothing width = 50 Angstrom
    Gaussian kernel width used to derive local continuum for spectral normalization (Section 2).
  • Uncertainty scaling polynomial coefficients = 3rd-order polynomial fit
    Scales formal fitting uncertainties to match repeat-observation dispersion as function of S/N, Teff, [Fe/H] for dwarfs and giants (Section 5.3).
assumptions (5)
  • domain assumption GALAH DR2 and APOGEE-Payne labels are sufficiently accurate to serve as training references.
    The entire method transfers these labels to LAMOST spectra; any systematic errors in the training labels propagate to the catalog (Section 3).
  • domain assumption Kurucz model gradient spectra accurately represent the true physical response of stellar spectra to label changes.
    These gradients regularize the training (Eq. 2) and define the physicality criterion (Section 5.4.2); if they are inaccurate for some elements or regimes, the physical claim fails.
  • domain assumption Neural network interpolator generalizes across the ~20-dimensional label space with ~4,500 to 15,000 training stars.
    The model must interpolate to stars not near reference gradient points; overfitting or poor extrapolation would bias labels (Section 2).
  • domain assumption Averaged LAMOST LSF is sufficient; fiber-to-fiber and plate-to-plate LSF variations are negligible.
    The authors adopt a wavelength-dependent average LSF and defer per-fiber analysis to future work (Section 2).
  • domain assumption Isochrone-based recalibration of Teff and logg using Gaia parallax and photometry is accurate.
    Training Teff/logg are corrected with Bayesian isochrone fitting (Appendix A); the correction is applied before training and affects all derived parameters.

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Cite this review

Pith. "Pith review of Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra." pith.science (2026). https://pith.science/paper/VGRLWH5R

@misc{pith2026190809727,
  author       = {Pith},
  title        = {Pith review of: Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VGRLWH5R}},
  note         = {Machine review of arXiv:1908.09727}
}
abstract

We present the determination of stellar parameters and individual elemental abundances for 6 million stars from $\sim$8 million low-resolution ($R\sim1800$) spectra from LAMOST DR5. This is based on a modeling approach that we dub $The$ $Data$--$Driven$ $Payne$ ($DD$--$Payne$), which inherits essential ingredients from both {\it The Payne} \citep{Ting2019} and $The$ $Cannon$ \citep{Ness2015}. It is a data-driven model that incorporates constraints from theoretical spectral models to ensure the derived abundance estimates are physically sensible. Stars in LAMOST DR5 that are in common with either GALAH DR2 or APOGEE DR14 are used to train a model that delivers stellar parameters ($T_{\rm eff}$, $\log g$, $V_{\rm mic}$) and abundances for 16 elements (C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, and Ba) when applied to LAMOST spectra. Cross-validation and repeat observations suggest that, for ${\rm S/N}_{\rm pix}\ge 50$, the typical internal abundance precision is 0.03--0.1\,dex for the majority of these elements, with 0.2--0.3\,dex for Cu and Ba, and the internal precision of $T_{\rm eff}$ and $\log g$ is better than 30\,K and 0.07\,dex, respectively. Abundance systematics at the $\sim$0.1\,dex level are present in these estimates, but are inherited from the high-resolution surveys' training labels. For some elements, GALAH provides more robust training labels, for others, APOGEE. We provide flags to guide the quality of the label determination and to identify binary/multiple stars in LAMOST DR5. The abundance catalogs are publicly accessible via \href{url}{http://dr5.lamost.org/doc/vac}.

Figures

Figures reproduced from arXiv: 1908.09727 by the authors.

Figure 1
Figure 1. Comparison of gradient spectra from the DD–P ayne using the LAMOST–GALAH training set (red) with those of the Kurucz spectral model (black) for a fiducial star with Teff = 4821 K, log g = 2.5, [Fe/H] = 0, and [X/Fe] = 0. The gradient spectra are generated based on normalized spectra, utilizing the same local-continuum normalization algorithm as that of Ho et al. (2017). The very broad features presented in the gradi… view at source ↗
Figure 2
Figure 2. Color-coded correlation coefficients between the gradient spectra of the best-fit DD–P ayne model and those of the Kurucz models for the closest reference star. The correlation is shown for LAMOST DR5 stars across the Teff –[Fe/H] plane, and the color scale indicates the median value of correlation coefficients for all stars in the bin. For Li, Na, Mg, Al, Si, Sc, Ti, V, Cr, Mn, Co, Zn, Y, Ba, and Eu, the DD-P ayne … view at source ↗
Figure 3
Figure 3. Covariances among different labels of the gradient spectra resulting from the DD–P ayne models. The spectral models are trained using the LAMOST–GALAH/APOGEE overlapping sample (see [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (23 more)
Figure 4
Figure 4. Figure 4: Left: Footprints of LAMOST DR5 (grey) and GALAH DR2 (black) in Galactic coordinates (l, b) centered at the Galactic anti-center (l = 180◦ , b = 0◦ ). Common areas between LAMOST and GALAH are shown in red. In total, there are 14,619 stars in common. Right: Footprints o…
Figure 5
Figure 5. Figure 5: Distributions of the DD–P ayne training samples in the Teff –log g plane. Top left: Distribution of the LAMOST–GALAH training stars in the Kiel diagram. The Teff and log g values are from a corrected version of the GALAH DR2 values exploiting the Gaia parallax and mult…
Figure 6
Figure 6. Figure 6: Cross-validations of Teff , log g, and [Fe/H] for DD–P ayne results derived using the LAMOST–GALAH training set (left) and results derived using the LAMOST–APOGEE training set (right). The cross-validation data sets are not used during the training of the DD– P ayne. F…
Figure 7
Figure 7. Figure 7: Cross-validations of the DD–P ayne elemental abundances derived with the LAMOST–GALAH training set. The number of stars adopted for cross-validation, as well as the standard deviation between our estimates from the LAMOST spectra and the GALAH DR2 values, are marked in…
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Internal precision of Teff and log g derived from repeat observations as a function of S/N. The internal precision is defined as the rms standard deviation of the repeat observations. Each dot is deduced from O(1000) stars with repeat observations. Stars are classified…
Figure 10
Figure 10. Figure 10: Internal precision of elemental abundances deduced from repeat observations when the LAMOST–GALAH training set is adopted. The internal precision is defined as the rms standard deviation of the repeat observations. The top panel shows how the precision varies as a fun…
Figure 11
Figure 11. Figure 11: Similar to [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]
Figure 12
Figure 12. Figure 12: Comparison of the DD–P ayne-derived LAMOST abundances, by adopting either the LAMOST–GALAH training set (labeled as “LAMOST DD-Payne G”) or the LAMOST–APOGEE training set (labeled as “LAMOST DD-Payne A”). The data points show all the dwarf stars, overlaid by the conto…
Figure 13
Figure 13. Figure 13: Inferred LAMOST stellar abundances as a function of Teff for stars with solar metallicity (−0.2 < [Fe/H] < 0.2). The left column shows the results from the LAMOST–GALAH training set, and the right column the LAMOST–APOGEE training set. The background and contours repr…
Figure 14
Figure 14. Figure 14: The Teff –log g and [Fe/H]–[α/Fe] diagrams for a sample of 2,932,585 LAMOST DR5 stars that have good S/N (>30) and decent DD–P ayne fits (qflag chi2 = “good”). Both figures are color-coded by the stellar number density. We define [α/Fe] to be the average of [Mg/Fe], […
Figure 15
Figure 15. Figure 15: Elemental abundance distributions of dwarf stars in the [X/Fe]–[Fe/H] plane. All subplots are color-coded with the stellar number density. Only the recommended set of abundances are shown. Since stars with [X/Fe] flag = 0 are discarded, different elements can have sli…
Figure 16
Figure 16. Figure 16: Same as [PITH_FULL_IMAGE:figures/full_fig_p028_16.png]
Figure 17
Figure 17. Figure 17: Comparison of the [Fe/H] and [X/Fe] distribution be￾tween presumed single stars (black) and binary/multiple stars (red). Shown are stars with 5200 < Teff < 5800 K and S/N > 50. Since both subsets should trace the same Galactic chemical evolution, we expect the two dis…
Figure 18
Figure 18. Figure 18: Comparison of Teff and log g for the GALAH DR2 (lef t panels) and APOGEE–P ayne (right panels) catalog values with the corrected values used in this work. The correction adopts a Bayesian framework with Gaia parallax and multi-band photometry as extra constraints. In …
Figure 19
Figure 19. Figure 19: Comparison of the DD–P ayne empirical gradient spectra (black) to the ab initio calculations from the Kurucz models (red). The left panels show the overview of the full optical range, and the right panels zoom in on some of the most prominent features for each element…
Figure 20
Figure 20. Figure 20: Continuation for [PITH_FULL_IMAGE:figures/full_fig_p038_20.png]
Figure 21
Figure 21. Figure 21: Continuation for [PITH_FULL_IMAGE:figures/full_fig_p039_21.png]
Figure 22
Figure 22. Figure 22: Similar to [PITH_FULL_IMAGE:figures/full_fig_p040_22.png]
Figure 23
Figure 23. Figure 23: Continuation for [PITH_FULL_IMAGE:figures/full_fig_p041_23.png]
Figure 24
Figure 24. Figure 24: Continuation for [PITH_FULL_IMAGE:figures/full_fig_p042_24.png]
Figure 25
Figure 25. Figure 25: Comparison of the GALAH DR2 and APOGEE-P ayne abundances for the common stars between GALAH and APOGEE. Dwarfs (black) and giants (red) are shown in different colors. The numbers in each panel mark the median and standard deviation. We select only stars with reliable …
Figure 26
Figure 26. Figure 26: GALAH DR2 and APOGEE–P ayne abundances as a function of Teff for stars with solar metallicity (−0.2 < [Fe/H] < 0.2). We select only stars with reliable GALAH (flag == 0) and APOGEE–P ayne (quality flag == “good”) determination. The figure demonstrates that, for some e…

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  3. Unveiling the nature of G6096: a likely hierarchical triple system

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Reference graph

Works this paper leans on

100 extracted references · 68 canonical work pages · cited by 3 Pith papers

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ""...

  3. [3]

    ݝ4i xkN q[m4s @<HCw' rx yZvVp9@! )vVp9@! )vVp9@! )[> 7P` 햆Vd9@! )m+ S M𺄋aih L! ]4yg

    thebibliography [1] 20pt to REFERENCES 6pt =0pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command Each re...

    2017

  4. [4]

    Abazajian K. N. et al. , 2009, , 182, 543

    2009

  5. [5]

    Alam S. et al. , 2015, , 219, 12

    2015

  6. [6]

    J., Scott P., 2009, , 47, 481

    Asplund M., Grevesse N., Sauval A. J., Scott P., 2009, , 47, 481

    2009

  7. [7]

    Battistini C., Bensby T., 2015, , 577, A9

    2015

  8. [8]

    S., 2014, , 562, A71

    Bensby T., Feltzing S., Oey M. S., 2014, , 562, A71

    2014

Show all 100 references
  1. [9]

    Bland-Hawthorn J. et al. , 2019, Monthly Notices of the Royal Astronomical Society, 486, 1167

    2019

  2. [10]

    Bressan A., Marigo P., Girardi L., Salasnich B., Dal Cero C., Rubele S., Nanni A., 2012, , 427, 127

    2012

  3. [11]

    Bu Y., Pan J., 2015, , 447, 256

    2015

  4. [12]

    Buder S. et al. , 2018, , 478, 4513

    2018

  5. [13]

    Casagrande L., Ram \' rez I., Mel \'e ndez J., Bessell M., Asplund M., 2010, , 512, A54

    2010

  6. [14]

    R., Hogg D

    Casey A. R., Hogg D. W., Ness M., Rix H.-W., Ho A. Q., Gilmore G., 2016, arXiv 1603.03040

    2016 arXiv

  7. [15]

    Castelli F., 2005, Memorie della Societa Astronomica Italiana Supplementi, 8, 25

    2005

  8. [16]

    L., 2003, in IAU Symposium, Vol

    Castelli F., Kurucz R. L., 2003, in IAU Symposium, Vol. 210, Modelling of Stellar Atmospheres, Piskunov N., Weiss W. W., Gray D. F., eds., p. A20

    2003

  9. [17]

    D., 2016, , 823, 102

    Choi J., Dotter A., Conroy C., Cantiello M., Paxton B., Johnson B. D., 2016, , 823, 102

    2016

  10. [18]

    Cropper M. et al. , 2018, , 616, A5

    2018

  11. [19]

    Dalton G. et al. , 2014, in , Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, p. 91470L

    2014

  12. [20]

    de Jong R. S. et al. , 2019, The Messenger, 175, 3

    2019

  13. [21]

    De Silva G. M. et al. , 2015, , 449, 2604

    2015

  14. [22]

    J., Marques J

    Deal M., Alecian G., Lebreton Y., Goupil M. J., Marques J. P., LeBlanc F., Morel P., Pichon B., 2018, , 618, A10

    2018

  15. [23]

    Deng L.-C. et al. , 2012, Research in Astronomy and Astrophysics, 12, 735

    2012

  16. [24]

    W., 2008, , 178, 89

    Dotter A., Chaboyer B., Jevremovi \'c D., Kostov V., Baron E., Ferguson J. W., 2008, , 178, 89

    2008

  17. [25]

    Dotter A., Conroy C., Cargile P., Asplund M., 2017, , 840, 99

    2017

  18. [26]

    Duch \^e ne G., Kraus A., 2013, , 51, 269

    2013

  19. [27]

    R., Bergemann M., Cargile P., Conroy C., Eilers A.-C., 2018, , 473, 5043

    El-Badry K., Rix H.-W., Ting Y.-S., Weisz D. R., Bergemann M., Cargile P., Conroy C., Eilers A.-C., 2018, , 473, 5043

    2018

  20. [28]

    Evans D. W. et al. , 2018, , 616, A4

    2018

  21. [29]

    Feltzing S. et al. , 2018, in IAU Symposium, Vol. 334, Rediscovering Our Galaxy, Chiappini C., Minchev I., Starkenburg E., Valentini M., eds., pp. 225--232

    2018

  22. [30]

    L., 1999, , 111, 63

    Fitzpatrick E. L., 1999, , 111, 63

    1999

  23. [31]

    W., 2018, , 865, 96

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

    2018

  24. [32]

    , 2018, , 616, A1

    Gaia Collaboration et al. , 2018, , 616, A1

    2018

  25. [33]

    , 2016, , 595, A1

    Gaia Collaboration et al. , 2016, , 595, A1

    2016

  26. [34]

    Gao S., Liu C., Zhang X., Justham S., Deng L., Yang M., 2014, , 788, L37

    2014

  27. [35]

    Gao X. et al. , 2018, , 481, 2666

    2018

  28. [36]

    Gilmore G. et al. , 2012, The Messenger, 147, 25

    2012

  29. [37]

    A., Weinberg D

    Griffith E., Johnson J. A., Weinberg D. H., 2019, arXiv 1908.06113, arXiv:1908.06113

    2019 arXiv

  30. [38]

    Ho A. Y. Q. et al. , 2017, , 836, 5

    2017

  31. [39]

    Holtzman J. A. et al. , 2018, , 156, 125

    2018

  32. [40]

    Huang Y., Liu X.-W., Yuan H.-B., Xiang M.-S., Chen B.-Q., Zhang H.-W., 2015, , 454, 2863

    2015

  33. [41]

    Jofr \'e P., Heiter U., Soubiran C., 2018, arXiv e-prints, arXiv:1811.08041

    2018 arXiv

  34. [42]

    J \"o nsson H. et al. , 2018, , 156, 126

    2018

  35. [43]

    Kollmeier J. A. et al. , 2017, arXiv 1711.03234

    2017 arXiv

  36. [44]

    J., Grundahl F., Richard O., Mashonkina L., Barklem P

    Korn A. J., Grundahl F., Richard O., Mashonkina L., Barklem P. S., Collet R., Gustafsson B., Piskunov N., 2007, , 671, 402

    2007

  37. [45]

    L., 1970, SAO Special Report, 309

    Kurucz R. L., 1970, SAO Special Report, 309

    1970

  38. [46]

    L., 1993, SYNTHE spectrum synthesis programs and line data

    Kurucz R. L., 1993, SYNTHE spectrum synthesis programs and line data

    1993

  39. [47]

    L., 2005, Memorie della Societa Astronomica Italiana Supplementi, 8, 14

    Kurucz R. L., 2005, Memorie della Societa Astronomica Italiana Supplementi, 8, 14

    2005

  40. [48]

    W., Bovy J., 2019 a , , 483, 3255

    Leung H. W., Bovy J., 2019 a , , 483, 3255

    2019

  41. [49]

    W., Bovy J., 2019 b , arXiv e-prints

    Leung H. W., Bovy J., 2019 b , arXiv e-prints

    2019

  42. [50]

    Li J. et al. , 2016, Research in Astronomy and Astrophysics, 16, 110

    2016

  43. [51]

    Li X., Lu Y., Comte G., Luo A., Zhao Y., Wang Y., 2015, , 218, 3

    2015

  44. [52]

    Lind K., Primas F., Charbonnel C., Grundahl F., Asplund M., 2009, , 503, 545

    2009

  45. [53]

    Lindegren L. et al. , 2018, , 616, A2

    2018

  46. [54]

    Liu C. et al. , 2015, , 807, 4

    2015

  47. [55]

    Liu X.-W. et al. , 2014, in IAU Symposium, Vol. 298, Setting the scene for Gaia and LAMOST, Feltzing S., Zhao G., Walton N. A., Whitelock P., eds., pp. 310--321

    2014

  48. [56]

    Liu X.-W., Zhao G., Hou J.-L., 2015, Research in Astronomy and Astrophysics, 15, 1089

    2015

  49. [57]

    Luo A.-L. et al. , 2015, Research in Astronomy and Astrophysics, 15, 1095

    2015

  50. [58]

    Luri X. et al. , 2018, , 616, A9

    2018

  51. [59]

    Majewski S. R. et al. , 2017, , 154, 94

    2017

  52. [60]

    V., Gorbaneva T

    Mishenina T. V., Gorbaneva T. I., Basak N. Y., Soubiran C., Kovtyukh V. V., 2011, Astronomy Reports, 55, 689

    2011

  53. [61]

    Munari U. et al. , 2014, , 148, 81

    2014

  54. [62]

    Nataf D. M. et al. , 2019, , 158, 14

    2019

  55. [63]

    W., Rix H.-W., Ho A

    Ness M., Hogg D. W., Rix H.-W., Ho A. Y. Q., Zasowski G., 2015, , 808, 16

    2015

  56. [64]

    E., Chen Y

    Nissen P. E., Chen Y. Q., Carigi L., Schuster W. J., Zhao G., 2014, , 568, A25

    2014

  57. [65]

    E., Gustafsson B., 2018, Astronomy and Astrophysics Review, 26, 6

    Nissen P. E., Gustafsson B., 2018, Astronomy and Astrophysics Review, 26, 6

    2018

  58. [66]

    \"O nehag A., Gustafsson B., Korn A., 2014, , 562, A102

    2014

  59. [67]

    A., 2017, , 597, A16

    Piskunov N., Valenti J. A., 2017, , 597, A16

    2017

  60. [68]

    Re Fiorentin P., Bailer-Jones C. A. L., Lee Y. S., Beers T. C., Sivarani T., Wilhelm R., Allende Prieto C., Norris J. E., 2007, , 467, 1373

    2007

  61. [69]

    Rix H.-W., Bovy J., 2013, , 21, 61

    2013

  62. [70]

    W., 2016, , 826, L25

    Rix H.-W., Ting Y.-S., Conroy C., Hogg D. W., 2016, , 826, L25

    2016

  63. [71]

    Skrutskie M. F. et al. , 2006, , 131, 1163

    2006

  64. [72]

    Souto D. et al. , 2019, , 874, 97

    2019

  65. [73]

    Steinmetz M. et al. , 2006, , 132, 1645

    2006

  66. [74]

    I., Adibekyan V

    Su \'a rez-Andr \'e s L., Israelian G., Gonz \'a lez Hern \'a ndez J. I., Adibekyan V. Z., Delgado Mena E., Santos N. C., Sousa S. G., 2016, , 591, A69

    2016

  67. [75]

    Ting Y.-S., Conroy C., Goodman A., 2015, , 807, 104

    2015

  68. [76]

    Ting Y.-S., Conroy C., Rix H.-W., Asplund M., 2018, , 860, 159

    2018

  69. [77]

    Ting Y.-S., Conroy C., Rix H.-W., Cargile P., 2017 a , , 843, 32

    2017

  70. [78]

    Ting Y.-S., Conroy C., Rix H.-W., Cargile P., 2019, , 879, 69

    2019

  71. [79]

    Ting Y.-S., Rix H.-W., Conroy C., Ho A. Y. Q., Lin J., 2017 b , , 849, L9

    2017

  72. [80]

    Wang R. et al. , 2019, , 131

    2019

  73. [81]

    Wright E. L. et al. , 2010, , 140, 1868

    2010

  74. [82]

    Wu Y. et al. , 2011, Research in Astronomy and Astrophysics, 11, 924

    2011

  75. [83]

    Xiang M. et al. , 2017 a , , 232, 2

    2017

  76. [84]

    Xiang M. et al. , 2018, , 237, 33

    2018

  77. [85]

    Xiang M.-S. et al. , 2017 b , , 464, 3657

    2017

  78. [86]

    Xiang M.-S. et al. , 2015, , 448, 822

    2015

  79. [87]

    Yang T., Li X., 2015, , 452, 158

    2015

  80. [88]

    Yanny B. et al. , 2009, , 137, 4377

    2009

  81. [89]

    York D. G. et al. , 2000, , 120, 1579

    2000

  82. [90]

    Yuan H., Liu X., Xiang M., Huang Y., Chen B., 2015 a , , 799, 134

    2015

  83. [91]

    Yuan H., Liu X., Xiang M., Huang Y., Chen B., Wu Y., Hou Y., Zhang Y., 2015 b , , 799, 135

    2015

  84. [92]

    Yuan H.-B. et al. , 2015 c , , 448, 855

    2015

  85. [93]

    Yuan H.-B., Liu X.-W., Xiang M.-S., 2013, , 430, 2188

    2013

  86. [94]

    Zasowski G. et al. , 2017, , 154, 198

    2017

  87. [95]

    Zhang H.-H., Liu X.-W., Yuan H.-B., Zhao H.-B., Yao J.-S., Zhang H.-W., Xiang M.-S., Huang Y., 2014, Research in Astronomy and Astrophysics, 14, 456

    2014

  88. [96]

    Q., Wang Q

    Zhang X., Zhao G., Yang C. Q., Wang Q. X., Zuo W. B., 2019, , 131, 094202

    2019

  89. [97]

    Zhao G. et al. , 2016, , 833, 225

    2016

  90. [98]

    Zhao G., Zhao Y.-H., Chu Y.-Q., Jing Y.-P., Deng L.-C., 2012, Research in Astronomy and Astrophysics, 12, 723

    2012

  91. [99]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence a...

  92. [100]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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