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Estimating Stellar Atmospheric Parameters and [{\alpha}/Fe] for LAMOST O-M type Stars Using a Spectral Emulator

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper demonstrates that a MaStar-based spectral emulator with grouping optimization derives Teff, log g, [Fe/H], and [alpha/Fe] for LAMOST O-M stars, producing a 10.3-million-spectrum catalog in about 70 hours.

desk verdict A useful, transparent pipeline paper with a real speed-up trick, but the final catalog is a hybrid of two label sets and the 'homogeneous' claim is overstated. read the letter →

arxiv 2411.08551 v1 pith:DUZL5OMD submitted 2024-11-13 astro-ph.SR astro-ph.GAastro-ph.IM

Jun-chao Liang , A-Li Luo , Yin-Bi Li , Xiao-Xiao Ma , Shuo Li , Shu-Guo Ma , Hai-Ling Lu , Yun-Jin Zhang
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Bing Du Xiao Kong
This is my paper · ORCID
classification astro-ph.SRastro-ph.GAastro-ph.IM
keywords spectralemulatorGaussianprocessregressionstellaratmosphericparametersLAMOSTMaStarprincipalcomponentanalysisBayesianoptimizationO-Mtypestars
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 spectral emulator built from the MaStar empirical stellar library, combined with a grouping optimization strategy, can derive effective temperature, surface gravity, metallicity, and alpha-element abundance for the full range of O- through M-type stars in LAMOST low-resolution spectra. It reports producing a catalog of 10,344,033 spectra, about 90% of LAMOST DR10, with internal dispersions of 15-594 K in temperature, 0.03-0.27 dex in log g, 0.02-0.10 dex in [Fe/H], and 0.01-0.04 dex in [alpha/Fe]. If correct, this would give LAMOST a homogeneous parameter set across spectral types, filling gaps where official pipelines are weak for hot OBA stars and cool M stars, and would cut the cost of spectral fitting roughly tenfold on a single machine. The paper also uses external comparisons to flag problems in MaStar's median log g, particularly for cool giants, and patches the catalog by repredicting with log g labels from Imig et al. (2022).

What carries the argument

The central mechanism is the spectral emulator: a machine-learning map from stellar parameters (Teff, log g, [Fe/H], [alpha/Fe]) to a PCA-compressed spectrum, built with Gaussian process regression using a radial-basis-function plus constant kernel. The companion mechanism is the grouping optimization strategy, which groups similar LAMOST spectra into 'concatenated spectra' of Ngroup=1000, matches each group to the t=100 nearest MaStar parameter sets, projects those into a low-dimensional principal component space, and uses Bayesian optimization to minimize the chi-square between observed and emulated spectra.

What would settle it

Take a sample of cool giants from the recommended catalog with independent asteroseismic or eclipsing-binary surface gravities and compare the catalog's log g values; if the systematic underestimation persists by more than about 0.2 dex after the Imig et al. patch, then the MaStar median log g bias is not fully removed and the claim of homogeneous reliable parameters fails for giants.

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Extended reading notes

Core claim

The central discovery claimed is that a Gaussian-process spectral emulator trained on MaStar spectra, with PCA compression, can act as a fast generative model of stellar spectra, and that fitting the 237 principal components through a grouped Bayesian chi-square minimization yields reliable parameters across the entire O-M range. The paper demonstrates this by reproducing MaStar spectra to a dispersion of 0.01 in normalized flux and comparing the LAMOST predictions against APOGEE, PASTEL, LASP, LASPM, HotPayne, Gaia-ESO, and open-cluster member stars. The comparisons show good agreement for dwarfs and FGK stars but reveal a systematic log g underestimation of about 0.35 dex for M giants and 0.5 dex for cold FGK giants, which the paper attributes to the median log g values adopted by the MaStar catalog. The paper's conclusion is that this is the first demonstration of a spectral emulator deriving Teff, log g, [Fe/H], and [alpha/Fe] for O-M type stars from LAMOST low-resolution spectra, and that the resulting recommended catalog is a first step toward an empirical spectral library for LAMOST.

Load-bearing premise

The pipeline treats the MaStar DR17 median atmospheric parameters, especially median log g, as reliable training labels; the paper's own external comparisons show log g is systematically underestimated by about 0.35 dex for M giants and 0.5 dex for cold FGK giants relative to APOGEE, and the catalog is only corrected by swapping in Imig et al. (2022) log g labels.

Editorial extensions

If this is right

  • LAMOST would gain homogeneous atmospheric parameters for O-M type stars, including OB-type stars that the official LASP and LASPM catalogs do not reliably cover.
  • Empirical-library fitting avoids some synthetic-model mismatches for cool M giants; the paper's comparison with APOGEE shows improved log g and [Fe/H] agreement over LASPM, especially for giants.
  • The method is fast enough for survey-scale use, processing about 11.4 million spectra in under 70 hours on one machine, roughly ten times faster than ungrouped emulator fitting.
  • The recommended catalog includes quality flags for S/N, chi-square, and [alpha/Fe], giving users explicit warnings on unreliable regimes such as hot stars with weak alpha-feature sensitivity.
  • The exposed MaStar median log g bias is corrected by repredicting with Imig et al. (2022) log g labels, and future MaStar calibration updates are expected to propagate into improved LAMOST catalogs.

Reading between the lines

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

  • If MaStar labels are biased in parameter regions not covered by the external comparisons, the catalog inherits that bias; this could be tested by comparing predicted log g against asteroseismic or eclipsing-binary log g for a broader sample of giants.
  • The grouping optimization relies on the LAMOST 1D spectral classification prior, so classification errors would propagate into grouping and final parameters; a test would be re-grouping with independent classifications and measuring parameter shifts.
  • The same grouping-optimization trick could accelerate MCMC-based parameter estimation with emulators, which the paper itself identifies as a natural next step for obtaining more realistic posterior uncertainties.
  • The roughly 0.2% of FGK stars with strong emission lines or unmasked bad pixels suggests that adding an outlier-detection preprocessing step could substantially improve the reliability of the recommended catalog.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper develops a spectral emulator based on the MaStar stellar library and Gaussian process regression, combined with a grouping optimization strategy, to estimate Teff, log g, [Fe/H], and [α/Fe] for 10,344,033 LAMOST DR10 low-resolution spectra of O-M type stars. The workflow preprocesses MaStar and LAMOST spectra, performs PCA dimensionality reduction, trains a GPR emulator, groups LAMOST spectra by spectral class and initial parameters, and uses Bayesian optimization in a reduced-parameter space. The authors validate their results against APOGEE, PASTEL, HotPayne, Gaia-ESO, and open clusters, report internal error models, and provide a public recommended catalog with quality flags. They report that the method runs in about 70 hours on a single machine and claim the first application of a spectral emulator to O-M type LAMOST stars.

Significance. The paper addresses a real need: LAMOST lacks homogeneous atmospheric parameters for hot and cool stars, and existing pipelines have known deficiencies. The main strengths are (1) a large public catalog that includes OBA- and M-type parameters, (2) a quantified efficiency gain of about 10x over non-grouped spectral fitting, and (3) a detailed external validation strategy against several independent datasets. These are substantial contributions, and the workflow is described in enough detail to be reproduced. However, the central reliability claim is not yet established: the final catalog mixes parameters from two different training-label sets, and the quoted internal errors do not include systematic label bias. The paper is therefore a promising methods paper with a useful catalog, but the advertised homogeneity and accuracy need additional work.

major comments (4)
  1. [Section 4.3 and Table 2] The internal error model uses GPRsys trained on MaStar prediction errors (Figure 7), i.e., it treats the MaStar DR17 median labels as ground truth. This cannot capture systematic label bias, and the paper's own external comparisons show offsets comparable to or larger than the Table 2 internal errors: gM log g is biased by -0.35 dex with 0.30 dex scatter (Figure 14), cold FGK giants show about 0.5 dex log g underestimation (Section 4.6.2), dM [α/Fe] offset is +0.16 dex (Figure 15), and Praesepe [Fe/H] is 0.25 dex below the literature value (Section 4.6.4). The Table 2 errors therefore understate the true catalog uncertainty, and the claim that internal error dispersions for log g are in the range 0.03-0.27 dex is misleading without a separate systematic-error budget.
  2. [Section 4.7 and Table 1] The final recommended catalog assigns log g from a model trained on Imig et al. (2022) labels, while Teff, [Fe/H], and [α/Fe] come from a model trained on MaStar DR17 median labels. This mixing of training-label sets invalidates the 'homogeneous parameters' claim in the Conclusions. Figure 28 also shows that the Imig-based log g still has a +0.24 dex offset for gM stars versus APOGEE. A homogeneous catalog would require retraining all four parameters on a single validated label set, or providing external systematic-error corrections for each parameter.
  3. [Section 4.6.4 and Figures 26-27] The cluster test does not support the claim that [Fe/H] is accurate at the 0.1 dex level. For Praesepe, the recommended catalog gives mean [Fe/H] = 0.00 dex while Fu et al. (2022) report 0.25 dex; the paper describes these as 'closely match[ing]', but the 0.25 dex offset is a large, unexplained systematic difference that is not included in the error model. This is a concrete example of label-driven systematics propagating into the catalog.
  4. [Section 4.6.3 and Figure 25] The Gaia-ESO validation is based on only 18 spectra and shows a Teff offset of -536 K with 1028 K scatter, and a -1260 K offset in the 10,000-20,000 K range. The conclusion that the recommended catalog supplies 'relatively reliable' OBA parameters is stronger than the evidence warrants. A larger high-resolution hot-star sample, or an explicit uncertainty flag for this parameter regime, is needed before the OBA part of the catalog can be used with confidence.
minor comments (5)
  1. [Section 3.3, step 5] Assigning normalized flux values exceeding 2 a value of 1 is an ad hoc clipping step; please justify its effect on the PCA training and on parameter recovery for emission-line stars.
  2. [Section 4.1, item 2] The sentence 'We adjusted the Ngroup to repeatly implement the workflow' contains a typo ('repeatly'), and the exact number of repeats used for the dispersion estimates in Figure 4 should be stated.
  3. [Section 4.3, Eq. (5)] The notation in Eq. (5) uses ∆(X) for the total uncertainty while ∆sys and ∆ran are used for the components; consider using σ(X) to avoid confusion with the parameter differences defined in Section 4.2.
  4. [Section 4.4, FLAG χ2] The FLAG χ2 criterion 'χ2 > LR(X)' is described only qualitatively; please specify the independent variables and training set used in the linear regression so that the flag is reproducible.
  5. [Throughout] The text contains several typographical errors, including 'specatra' in Section 4.6.2 and 'T raining set' in Section 4.3; a careful proofread is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: LAMOST predictions are out-of-sample emulator outputs validated against independent catalogs; the log g replacement is a calibration patch rather than a constructed prediction.

full rationale

The derivation chain is not circular in the operative sense. The spectral emulator is trained on MaStar DR17 spectra with median atmospheric parameters as labels, while LAMOST DR10 spectra are never used to fit the emulator or its hyperparameters; the reported LAMOST parameters are genuine out-of-sample predictions. The load-bearing validation is external and independent: comparisons with APOGEE DR16, PASTEL, Gaia-ESO, HotPayne, and the open clusters M67, M35, and Praesepe constrain the accuracy of the predicted parameters. The known log g offset identified in Sections 4.6.1 and 4.6.2 is attributed by the authors to MaStar's median log g and then patched in Section 4.7 by retraining with Imig et al. (2022) log g labels; this is a calibration correction, not a fitted input renamed as a prediction. The internal error model in Section 4.3 does use MaStar reproduction errors as a training set for GPRsys, but the paper explicitly calls this a theoretical lower bound and does not present it as external validation. Self-citations, such as Luo et al. (2015) and Du et al. (2012, 2021), support standard pipeline components (CFI initialization and the LASPM baseline) but are not the source of the central claim. Remaining concerns, including the mixed-label log g patch, the residual +0.24 dex offset for gM stars in Figure 28, and the acknowledged failures on strong emission lines and bad pixels, are accuracy and calibration limitations rather than circularity.

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

The central claim rests on MaStar label reliability, GPR interpolation quality, the PCA-space optimization equivalence, LAMOST redshift accuracy, and the independence of systematic and random errors. No new physical entities are introduced. Free parameters account for the emulator kernel, PCA truncation, grouping size, matching-prior intervals, and S/N cuts.

free parameters (7)
  • GPR kernel length scale = 1.5
    Chosen by cross-validation on MaStar; controls smoothness of the Teff-log g-[Fe/H]-[alpha/Fe] to spectrum mapping (Section 2.2).
  • GPR kernel amplitude = 2.1
    Chosen by cross-validation on MaStar; sets the variance scale of the emulator (Section 2.2).
  • PCA component counts = 237 for MaStar spectra (95% variance); m unspecified for parameter matrix (98% variance)
    Truncation thresholds chosen by hand to remove noise while reconstructing spectra; affects all reproduced spectra and optimization variables (Section 2.2).
  • Number of matched MaStar parameter sets t = 100
    Set from stability analysis of PCA boundaries and reproduction errors; t directly defines the PCA parameter space for optimization (Section 4.1).
  • Number of spectra per group Ngroup = 1000
    Set from time-accuracy tradeoff; grouping is central to the claimed roughly 10x speedup (Section 4.1, Figure 4).
  • S/N quality thresholds = M:10, K:8, G:8, F:8, A:5, OB:8/5
    Hand-selected thresholds in Figure 11 determine which spectra enter the recommended catalog (Section 4.4).
  • Prior matching intervals = Teff +/- 1000 K for Teff < 10000 K, +/- 5000 K for Teff >= 10000 K; log g +/- 1; [Fe/H] +/- 0.5; [alpha/Fe] +/- 0.2
    Ad hoc intervals in Section 2.2 step 4 restrict matched MaStar parameters before PCA; they directly shape the optimization domain.
assumptions (5)
  • domain assumption MaStar DR17 median parameters are reliable enough to serve as training labels.
    The emulator learns from MaStar parameters; Section 3.1 and Section 4.6 show systematic log g biases for cold giants relative to APOGEE, forcing a post-hoc replacement with Imig et al. (2022) log g.
  • domain assumption GPR with RBF plus constant kernel can interpolate the MaStar parameter-to-spectrum mapping within the training domain.
    The emulator is the core generative model (Section 2.2); no guarantee is given that the mapping is smooth and stationary across 3500-30,000 K with sparse hot-star coverage.
  • domain assumption The low-dimensional PCA parameter space preserves the chi-square landscape so Bayesian optimization finds the global optimum.
    Equation (3) optimizes in d-space after mapping matched MaStar parameters through PCA; the equivalence to the original parameter-space chi-square is assumed, not proven (Section 2.2 steps 4-6).
  • domain assumption LAMOST redshifts are accurate for rest-frame shifting.
    Section 3.3 step 2 shifts all LAMOST spectra to rest frame using LAMOST fits-header z; Section 4.7 notes Li et al. (2024, in preparation) finds radial-velocity issues for M-type stars, which would violate this assumption.
  • domain assumption Systematic and random errors are independent and can be added in quadrature.
    Eq. (5) combines GPRsys and GPRran assuming independence; Section 4.7 admits achieving independence is challenging, and no covariance check is provided.

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Cite this review

Pith. "Pith review of Estimating Stellar Atmospheric Parameters and [{\alpha}/Fe] for LAMOST O-M type Stars Using a Spectral Emulator." pith.science (2026). https://pith.science/paper/DUZL5OMD

@misc{pith2026241108551,
  author       = {Pith},
  title        = {Pith review of: Estimating Stellar Atmospheric Parameters and [\alpha/Fe] for LAMOST O-M type Stars Using a Spectral Emulator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DUZL5OMD}},
  note         = {Machine review of arXiv:2411.08551}
}
abstract

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for LAMOST. Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within $70$ hr. We observed that the internal error dispersions for T_eff, log g, [Fe/H], and [alpha/Fe] across different spectral types lie within the ranges of $15-594$ K, $0.03-0.27$ dex, $0.02-0.10$ dex, and $0.01-0.04$ dex, respectively, indicating a good consistency. A comparative analysis with external data highlighted deficiencies in the official LAMOST catalog and issues with MaStar parameters, as well as potential limitations of our method in processing spectra with strong emission lines and bad pixels. The derived atmospheric parameters as a part of this work are available at https://nadc.china-vo.org/res/r101402/ .

Figures

Figures reproduced from arXiv: 2411.08551 by the authors.

Figure 1
Figure 1. Diagram of the workflow for predicting atmospheric parameters. The dashed boxes show the training process of the spectral emulator using the MaStar library. The yellow boxes indicate mapping the stellar atmospheric parameter space to principal component space using PCA within each group of LAMOST spectra. The process from ‘grouping LAMOST spectra’ to ‘χ 2 convergence’ is termed the grouping optimization strategy. Th… view at source ↗
Figure 2
Figure 2. MaStar sample numbers as a function of Teff within prior parameter intervals for parameters of each MaStar spectrum. For Teff < 10, 000 K, the prior intervals are set to [Teff − 1000, Teff + 1000], [log g − 1, log g + 1], [[Fe/H]−0.5, [Fe/H]+0.5], [[α/Fe]−0.2, [α/Fe]+0.2]. For Teff ≥ 10, 000 K, the intervals are [Teff − 5000, Teff + 5000], with other settings consistent with Teff < 10, 000 K. this range with a step … view at source ↗
Figure 3
Figure 3. The median of the lower and upper boundaries of d1 across different Ngroup. The first and second rows represent the lower and upper boundaries of d1, respectively, and the different curves represent different spectral types. the other hand, we considered the stable optimiza￾tion boundaries of d to determine t. Specifically, we set Ngroup to [5, 2000] with a step size of 100, and the interval of t to [5, 1500] with a… view at source ↗
Figures from the paper (25 more)
Figure 4
Figure 4. Figure 4: Efficiency and accuracy of the grouping optimization strategy. The upper panel shows the time taken to predict atmospheric parameters in LAMOST DR10 for different Ngroup. The middle panel displays the dispersion of Teff errors in the grouping optimization strategy acro…
Figure 5
Figure 5. Figure 5: Examples of spectra generated by the spectral emulator. The yellow shading represents the uncertainty generated by the spectral emulator (defined in Equation (1)). 0:6 0:8 1:0 1:2 Normalized °ux (MaStar) 0:6 0:8 1:0 1:2 N o r m aliz e d ° u x ( M o d el) ¹ = 0; ¾ = 0:0…
Figure 6
Figure 6. Figure 6: Performance of the spectral emulator in different Teff bins. One-to-one plots compared the normalized flux in MaStar (horizontal axis) to the flux generated by the spectral emulator (vertical axis). µ and σ denote the sample mean and standard deviation, respectively, a…
Figure 7
Figure 7. Figure 7: Comparison of the performance of spectral emulators constructed by the MaStar, ELODIE, and MILES libraries. The horizontal axis represents spectral subclasses, and the vertical axis shows the dispersion of parameter errors (the predicted values minus true values) for d…
Figure 8
Figure 8. Figure 8: The histograms of the standard deviation for each column of ∆(PCA Teff), ∆(PCA log g), ∆(PCA [Fe/H]), and ∆(PCA [α/Fe]) using different grouping methods (t = 100). The red and blue histograms represent the results using the prior grouping (this work) and random groupin…
Figure 9
Figure 9. Figure 9: The median distribution of the absolute values in each column of ∆(PCA Teff) for different t values. Each panel includes 5000 median values corresponding to the same t value; the colors represent the density of the 5000 median values at each t. as ∆(PCA Teff), ∆(PCA lo…
Figure 10
Figure 10. Figure 10: Differences in atmospheric parameters from repeat spectral observations across M-type, FGK-type, and OBA￾type stars (columns (1)-(3), respectively) as a function of S/N. Only repeat observations carried out on different observation nights and with S/N differences of l…
Figure 11
Figure 11. Figure 11: Distribution of χ 2 values for different spectral types and the variation of S/N with χ 2 distribution. The spectral classifications were derived from the LAMOST spectral analysis pipeline (Luo et al. 2015). The horizontal axis represents the χ 2 values, the left y-ax…
Figure 12
Figure 12. Figure 12: The Teff−log g (left pannel) and [Fe/H]−[α/Fe] (right pannel) diagrams for the recommended catalog. Both figures are color-coded by the stellar number density. In the right panel, the red and black curves respectively represent the relationship between the median valu…
Figure 13
Figure 13. Figure 13: Comparison of the dM parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot. 3500 4000 4500 Pred (Te®) (K) 3500 4000 4500 A P O G E E ( T e …
Figure 14
Figure 14. Figure 14: Comparison of the gM parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot. ¡0:1 0 0:1 0:2 0:3 Pred ([®=Fe]) (dex) ¡0:1 0 0:1 0:2 0:3 A P O…
Figure 15
Figure 15. Figure 15: Comparison of the [α/Fe] in the recommended catalog with APOGEE DR16 in dM (left panel), gM (middle panel) and FGK-type (right panel) stars. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot.…
Figure 16
Figure 16. Figure 16: Comparison of the dM parameters in LASPM with APOGEE DR16. Mean and standard deviation of the parameter differences between LASPM and APOGEE are also shown in the plot. 3500 4000 4500 LASPM (Te®) (K) 3500 4000 4500 A P O G E E ( T e ® ) ( K ) ¹ = ¡173; ¾ = 102 2 5 10 …
Figure 17
Figure 17. Figure 17: Comparison of the gM parameters in LASPM with APOGEE DR16. Mean and standard deviation of the parameter differences between LASPM and APOGEE are also shown in the plot. Similar systematic discrepancies were observed in Li et al. (2021), who employed the BT-Stell inter…
Figure 18
Figure 18. Figure 18: Comparison of the FGK-type stars’ parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot. 4000 5000 6000 7000 Pred (Te®) (K) 4000 5000 6000 …
Figure 19
Figure 19. Figure 19: Comparison of the FGK-type stars’ parameters in the recommended catalog with PASTEL. Mean and standard deviation of the parameter differences between the recommended catalog and PASTEL are also shown in the plot. 4000 5000 6000 7000 8000 Pred (Te®) (K) 4000 5000 6000 …
Figure 20
Figure 20. Figure 20: Comparison of the FGK-type stars’ parameters in the recommended catalog with LAMOST. Mean and standard deviation of the parameter differences between the recommended catalog and LAMOST are also shown in the plot. atively high systematic error of 0.1 dex in comparison …
Figure 21
Figure 21. Figure 21: Examples of anomalous spectra. The horizontal axis represents the spectral wavelength, while the vertical axis of each subplot displays relative flux of the original spectra scaled to the range of 0-1 and the normalized flux. The black lines indicate the masked wavele…
Figure 22
Figure 22. Figure 22: Comparison of the A-type stars’ parameters in the recommended catalog with LAMOST. Mean and standard deviation of the parameter differences between the recommended catalog and LAMOST are also shown in the plot. K, 0.2 dex, and 0.1 dex, respectively. For A-type stars, …
Figure 23
Figure 23. Figure 23: Teff as a function of dereddened (BP-RP) color. The top left and top right panels, respectively, show the relationship between LASP’s Teff (for Teff < 7500 K), this work’s Teff, and the dereddened (BP-RP) color. The bottom left and bottom right panels respectively sho…
Figure 24
Figure 24. Figure 24: Comparison of the OBA-type stars’ parameters in the recommended catalog with HotPayne. Mean and standard deviation of the parameter differences between the recommended catalog and HotPayne are also shown in the plot. The error bars represent the uncertainty of each pa…
Figure 25
Figure 25. Figure 25: Comparison of the OBA-type stars’ parameters in the recommended catalog with Gaia-ESO. Mean and standard deviation of the parameter differences between the recommended catalog and Gaia-ESO are also shown in the plot. The error bars represent the uncertainty of each pa…
Figure 26
Figure 26. Figure 26: The distributions of Teff, log g, and [Fe/H] in the recommended catalog for M67 (left panel), M35 (middle panel), and Praesepe (right panel). The different colored curves represent PARSEC V2.0 isochrones of various ages. The [Fe/H] for the OCs were taken from Fu et al…
Figure 27
Figure 27. Figure 27: The distributions of Teff, log g, and [Fe/H] in Fu et al. (2022) for M67 (left panel), M35 (middle panel), and Praesepe (right panel). The different colored curves represent PARSEC V2.0 isochrones of various ages. The [Fe/H] for the OCs were taken from Fu et al. (2022…
Figure 28
Figure 28. Figure 28: Comparison of the log g in the recommended catalog (log g from Imig et al. 2022 replaces the MaStar’s median log g for predicting LAMOST parameters) with external data. The top left, top right, bottom left, and bottom right panels respectively represent the comparison…

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Reviewed August 12, 2026 · model on record in the stance chip above.