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Revisiting Ca II Activity Indices in FGK Stars: Systematic Biases in Infrared Triplet Measurements

T0 review · 1 major / 2 minor · reviewed 2026-05-10 · grok-4.3

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Pith's one-line read Photospheric templates underestimate the depth of Ca II infrared triplet cores, producing systematically negative residual activity indices in solar-like stars.

desk verdict The paper documents a systematic negative bias in IRT indices from template subtraction but leaves the physical mechanism for deeper cores under-explained. read the letter →

arxiv 2604.14642 v1 submitted 2026-04-16 astro-ph.SR

Xiaozhen Yang , Xiaoting Fu , Mingjie Jian , Jingkun Zhao , Hailong Yuan , Zhongrui Bai , Mengxin Wang , Yiqiao Dong
show 5 more authors
Mingkuan Yang Ziyue Jiang Qian Liu Ganyu Li Haotong Zhang
This is my paper · ORCID
classification astro-ph.SR
keywords CaIIinfraredtripletchromosphericactivitysynthetictemplatesubtractionFGKstarsresidualindicesLAMOSTNLTEeffectsmicroturbulentvelocity
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 examines why synthetic-template subtraction for Ca II activity measurements yields negative R+ indices for the infrared triplet in many FGK stars, even though the H&K lines behave as expected. Observational factors such as atmospheric-parameter mismatches, line-spread-function treatment, and error propagation create scatter but fail to account for the consistent underestimation of core depth. The authors conclude that the templates themselves are too shallow because they lack chromospheric structure and, to a lesser degree, non-local thermodynamic equilibrium effects. An empirical boost to microturbulent velocity deepens the model cores and reduces the offset, while indices from varied synthesis setups remain linearly correlated and therefore cross-calibratable.

What carries the argument

Synthetic-template subtraction used to isolate the chromospheric excess in Ca II lines and compute the residual activity index R+.

What would settle it

A direct comparison of observed IRT cores against synthetic spectra that explicitly include a chromospheric temperature rise or full NLTE treatment, checking whether the negative R+_IRT bias disappears across a large sample of inactive and active FGK stars.

Watch

Extended reading notes

Core claim

Synthetic photospheric templates underestimate the absorption depth of the Ca II IRT line cores in solar-like FGK stars, most likely because they omit chromospheric contributions and, to a lesser extent, NLTE effects. This template inadequacy produces the observed negative bias in the residual index R+_IRT, whereas the same procedure applied to the Ca II H&K lines does not show the same systematic offset.

Load-bearing premise

That parameter-matched synthetic spectra fully represent the pure photospheric contribution to the line cores without any chromospheric filling or NLTE adjustments.

Editorial extensions

If this is right

  • Atmospheric-parameter offsets and instrumental effects contribute only to random scatter, not the systematic negative bias.
  • Raising the microturbulent velocity in the synthetic models deepens the IRT cores and partially corrects the offset.
  • Activity indices derived from different synthesis configurations exhibit systematic zero-point shifts yet retain tight linear correlations, enabling cross-calibration across surveys.
  • The bias is intrinsic to the template construction rather than to the measurement process itself.

Reading between the lines

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

  • Survey pipelines that rely on standard photospheric libraries may need empirical microturbulence adjustments or hybrid photosphere-plus-chromosphere templates to avoid underestimating activity in the IRT.
  • The same template shortfall could subtly affect other infrared lines used for activity or abundance work in large spectroscopic catalogs.
  • Testing the proposed correction on stars with independent activity indicators, such as H-alpha or X-ray flux, would quantify how much of the residual scatter is removed.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 2 minor

Summary. The manuscript investigates systematic negative biases observed in Ca II infrared triplet (IRT) residual activity indices (R'_IRT) when subtracting synthetic photospheric templates from FGK star spectra. Using a uniform pipeline on solar-like stars from LAMOST DR9, MaStar, and XSL DR3, the authors measure R' indices for both Ca II H&K and IRT lines. They test and rule out contributions from atmospheric-parameter mismatches, instrumental line-spread function treatment, and propagated uncertainties. The central conclusion is that photospheric templates underestimate IRT core depths, likely due to missing chromospheric structure and NLTE effects; an empirical increase in microturbulent velocity is shown to deepen the synthetic cores and partially mitigate the negative offset. Different synthesis configurations produce offset but strongly correlated R' values, supporting cross-calibration.

Significance. If the observational result on the bias and its partial mitigation holds, the paper provides a useful clarification for chromospheric activity studies in large spectroscopic surveys, where the IRT is often preferred for its wavelength accessibility. Credit is due for the multi-survey approach, consistent measurement framework, and explicit tests of observational confounders, which support the empirical findings. The work aids interpretation of template-dependent systematics and offers a practical adjustment, though the physical attribution requires refinement.

major comments (1)
  1. [Abstract and Discussion] Abstract and Discussion: The claim that the negative R'_IRT bias arises because photospheric templates underestimate the depth of the IRT cores, 'likely owing to missing chromospheric structure and, to a lesser extent, NLTE effects,' conflicts with standard line-formation expectations. A chromospheric temperature rise increases the source function in the core, filling in the absorption and producing shallower observed cores than a pure-photosphere template; this predicts positive rather than negative residuals. The manuscript rules out parameter offsets, LSF, and noise but does not include forward modeling with chromospheric or NLTE-adjusted spectra to demonstrate that any such effect can produce deeper observed cores. The microturbulence adjustment is presented as an empirical mitigation rather than a test of the proposed mechanism, leaving the central attribution under-supported.
minor comments (2)
  1. [Methods] Methods: While the uniform pipeline is a strength, the exact definition of the R' index (including normalization and any scaling) and the specific wavelength windows for continuum and line measurements should be stated more explicitly to ensure full reproducibility across surveys.
  2. [Results/Figures] Figures: The correlation plots between R' values from different synthesis configurations would benefit from reporting the fitted slopes, intercepts, and scatter metrics to quantify the 'systematic offsets' described in the text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the value of the multi-survey empirical analysis. The major comment correctly identifies that our physical attribution of the negative R'_IRT bias is tentative and would benefit from clearer caveats. We address the concern directly below and will revise the manuscript to strengthen the discussion while preserving the observational results.

read point-by-point responses

  1. Referee: [Abstract and Discussion] Abstract and Discussion: The claim that the negative R'_IRT bias arises because photospheric templates underestimate the depth of the IRT cores, 'likely owing to missing chromospheric structure and, to a lesser extent, NLTE effects,' conflicts with standard line-formation expectations. A chromospheric temperature rise increases the source function in the core, filling in the absorption and producing shallower observed cores than a pure-photosphere template; this predicts positive rather than negative residuals. The manuscript rules out parameter offsets, LSF, and noise but does not include forward modeling with chromospheric or NLTE-adjusted spectra to demonstrate that any such effect can produce deeper observed cores. The microturbulence adjustment is presented as an empirical mitigation rather than a test of the proposed mechanism, leaving the central attribu

    Authors: We thank the referee for this insightful observation. Our conclusion is primarily empirical: after systematically excluding atmospheric-parameter mismatches, LSF treatment, and uncertainty propagation, the data show that observed IRT cores are deeper than those in parameter-matched photospheric templates. We agree that a simple chromospheric temperature rise would increase the source function and produce shallower cores (positive residuals), which is the opposite of what is observed. However, realistic chromospheric models incorporate additional physics—such as velocity fields, microturbulence enhancements, shocks, and NLTE level populations—that can deepen the cores for the Ca II IRT in certain regimes. The empirical v_turb increase we demonstrate acts as a proxy for these effects and partially removes the offset, providing supporting evidence even without full forward modeling. We acknowledge that the manuscript does not contain explicit chromospheric or NLTE forward models, which leaves the precise mechanism under-constrained. We will revise the abstract and discussion to (i) emphasize the empirical nature of the bias detection, (ii) note that the proposed attribution is tentative and requires dedicated modeling for confirmation, and (iii) clarify that the microturbulence adjustment is a practical mitigation rather than a direct test of the mechanism. These changes will be made without altering the reported measurements or the conclusion that the bias is intrinsic to the template subtraction. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; analysis is observational and data-driven

full rationale

The paper derives its central result—the existence and likely origin of a systematic negative bias in R+_IRT—by direct subtraction of parameter-matched synthetic templates from observed spectra across LAMOST DR9, MaStar, and XSL DR3 samples. Residual indices are computed and compared without any quantity being fitted to a subset and then re-labeled as a prediction. The empirical microturbulence adjustment is explicitly described as a post-hoc mitigation rather than a derived claim. No self-citations, uniqueness theorems, or ansatzes are invoked to close the argument; the negative bias is measured from the data residuals themselves, rendering the derivation self-contained.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The central claim rests on standard assumptions of stellar atmosphere modeling and the validity of synthetic-template subtraction as a baseline; the only ad-hoc adjustment is an empirical increase in microturbulent velocity.

free parameters (1)
  • microturbulent velocity increase
    Empirically raised to deepen synthetic IRT cores and reduce negative offset; value not specified numerically in abstract but treated as a tunable parameter.
assumptions (2)
  • domain assumption Synthetic photospheric templates accurately capture the continuum and line wings without chromospheric contributions
    Invoked when interpreting negative residuals as evidence of template inadequacy rather than measurement error.
  • domain assumption Atmospheric parameters from surveys are sufficiently accurate for template matching
    Tested but not fully eliminated as a contributor to scatter.

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Cite this review

Pith. "Pith review of Revisiting Ca II Activity Indices in FGK Stars: Systematic Biases in Infrared Triplet Measurements." pith.science (2026). https://pith.science/paper/2604.14642

@misc{pith2026260414642,
  author       = {Pith},
  title        = {Pith review of: Revisiting Ca II Activity Indices in FGK Stars: Systematic Biases in Infrared Triplet Measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.14642}},
  note         = {Machine review of arXiv:2604.14642}
}
read the original abstract

Synthetic-template subtraction is widely used to measure chromospheric activity in large spectroscopic surveys. However, many solar-like FGK stars show systematically negative Ca II infrared triplet (IRT) residual indices, implying that the observed line cores are deeper than those predicted by parameter-matched templates. We investigate this effect using solar-like stars from LAMOST DR9, MaStar, and XSL DR3, measuring activity indices (R+) for both the Ca II H&K and IRT lines in a uniform framework. We find that observational effects, including atmospheric-parameter offsets, treatment of the instrumental line-spread function, and propagated measurement uncertainties, contribute to scatter but do not explain the systematic negative bias in R+_IRT. The results instead suggest that the negative bias most likely arises because photospheric templates underestimate the depth of the IRT cores, likely owing to missing chromospheric structure and, to a lesser extent, NLTE effects. An empirical increase in the adopted microturbulent velocity deepens the synthetic IRT cores and partially mitigates the negative offset. In addition, R+ values derived from different synthesis configurations show systematic offsets but generally preserve strong linear correlations, indicating that they can be cross-calibrated. These results clarify the origin of negative Ca II IRT residual indices and help interpret template-dependent systematics in chromospheric activity measurements based on synthetic-template subtraction.

Figures

Figures reproduced from arXiv: 2604.14642 by the authors.

Figure 1
Figure 1. Distributions of Teff , log g, [Fe/H], and [α/Fe] for LAMOST DR9, MaStar, and XSL. The blue dashed line marks the median [Fe/H]cal of MaStar. Due to the small number of stars in XSL, its bin sizes are twice those of LAMOST and MaStar. rameters decrease significantly for spectra with SNRr ≥ 50 (signal-to-noise ratio in the r band). We therefore adopt SNRr ≥ 50 as the baseline quality criterion. We further restrict th… view at source ↗
Figure 2
Figure 2. LAMOST arc-lamp emission lines. The left panel shows the profile of the 4046.6 ˚A line and the right panel shows the profile of the 8591.3 ˚A line. Black points: arc-lamp spectrum; black dashed curve: cubic spline interpolation; red curve: fitted Gaussian. shape are present in some arc-lamp emission lines, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. R + HK and R + 8542 as functions of Teff , log g, and [Fe/H] for the LAMOST sample, derived using the A1 templates with Vmic = V t mic. The upper panels show R + HK, and the lower panels show R + 8542. The red star marks the Sun, for which R + HK = 0.0252 and R + 8542 = −0.0018. In the Teff panels, the red curve denotes the 5th-percentile relation. High-density regions are shown in yellow, and the median lines are p… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Variations in the normalized Ca II H&K and IRT line profiles as a function of Teff , log g, [Fe/H], and [α/Fe], using synthetic spectra computed with the A1 configuration. The upper panels show the Ca II H line, and the lower panels show the λ8542 line. Fixed parameter…
Figure 5
Figure 5. Figure 5: Atmospheric-parameter comparison for cross-matched samples. LAMOST parameters are shown on the x-axis, while the corresponding MaStar and APOGEE parameters are shown on the y-axis. Gray: LAMOST–MaStar cross-match. Large squares: LAMOST–MaStar–APOGEE triple match, with …
Figure 6
Figure 6. Figure 6: Variations of RHK (left panel) and R8542 (right panel) with Teff , log g, [Fe/H], and [α/Fe] for the A1 templates at LAMOST resolution. Different line styles denote different log g values, colors denote different [Fe/H] values, and marker types denote different [α/Fe] …
Figure 7
Figure 7. Figure 7: Comparison of R + 8542 measurements for the cross-matched LAMOST and MaStar samples under two convolution schemes. The left panel shows the case in which Gaussian convolution is applied to both datasets, while the right panel shows the case in which the LAMOST spectra …
Figure 8
Figure 8. Figure 8: Distributions of the estimated uncertainties, δR+ HK and δR+ 8542, derived from simulations based on the A1 templates. The Gaussian fits and their corresponding means and standard deviations are also shown. adding Gaussian noise with standard deviations equal to the co…
Figure 9
Figure 9. Figure 9: R + HK and R + 8542 as functions of Teff , log g, and [Fe/H] for the MaStar and XSL samples, derived using the A1 templates with Vmic = V t mic. The upper two rows show the MaStar sample, and the lower two rows show the XSL sample. The red star marks the Sun. In the Te…
Figure 10
Figure 10. Figure 10: presents a comparison of the Ca II λ8542 line between multiple template spectra and the solar spectrum. Although the A1 template performs best among the tested templates, reproducing the observed line wings while maintaining sufficiently deep line cores, its λ8542 lin…
Figure 11
Figure 11. Figure 11: Solar atmospheric structures of the ATLAS9, MARCS, and NewEra model atmospheres, compared with those of the semi-empirical quiet-Sun models VAL-C and ALC-7. The displayed quantities, distinguished by color, are the gas pressure pg, continuum optical depth at 500 nm, τ…
Figure 12
Figure 12. Figure 12: Comparison of the Ca II H&K and IRT line profiles between XSL spectra (black) and A1 templates (red). The left-hand panels show Vmic = V t mic, and the right-hand panels show Vmic = V t mic + 2 km s−1 . The upper panels display the hot, metal-rich star HD 102634, whil…
Figure 13
Figure 13. Figure 13: R + 8542 as functions of [Fe/H] for LAMOST, MaStar, and XSL when using A1 templates with Vmic = V t mic + 2 km s−1 . comparison to motivate our fiducial template configura￾tion. We then quantify the configuration dependence of R + HK and R + 8542 across a broader set …
Figure 14
Figure 14. Figure 14: Effects of different radiative transfer codes, model atmospheres, and line lists on the Ca II λ8542 profile under solar parameters. The black curve shows the observed solar spectrum. The reference iSpec configuration adopts SPECTRUM, MARCS, and VALD, and only one comp…
Figure 15
Figure 15. Figure 15: Correlations and residuals of R + 8542 for the LAMOST sample derived using different synthesis configurations, all evaluated relative to the A1 templates (x-axis). In each case, only one component of the A1 configuration is changed in order to isolate the effect of th…
Figure 16
Figure 16. Figure 16: 5th-percentile curves of R + HK and R + 8542 for the LAMOST solar-like sample. The upper two rows show R + HK, and the lower two rows show R + 8542. In each panel, the title lists the radiative transfer code and model atmosphere, while the colors distinguish the combi…

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Works this paper leans on

72 extracted references · 72 canonical work pages

  1. [1]

    The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

    Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    work page doi:10.3847/1538-4365/ac4414 2022

  2. [2]

    Model Atmospheres From Very Low Mass Stars to Brown Dwarfs

    Allard, F., Homeier, D., & Freytag, B. 2011, in Astronomical Society of the Pacific Conference Series, Vol. 448, 16th Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun, ed. C. Johns-Krull, M. K. Browning, & A. A. West, 91, doi: 10.48550/arXiv.1011.5405

    work page Pith review doi:10.48550/arxiv.1011.5405 2011

  3. [3]

    Rajpurohit, A. S. 2013, Memorie della Societa Astronomica Italiana Supplementi, 24, 128, doi: 10.48550/arXiv.1302.6559

    work page Pith review doi:10.48550/arxiv.1302.6559 2013

  4. [4]

    Near-infrared narrow-band photometry of M-giant and Mira stars: models meet observations

    Alvarez, R., & Plez, B. 1998, A&A, 330, 1109, doi: 10.48550/arXiv.astro-ph/9710157

    work page Pith review doi:10.48550/arxiv.astro-ph/9710157 1998

  5. [5]

    T., & Terranegra, L

    Andretta, V., Bus` a, I., Gomez, M. T., & Terranegra, L. 2005, A&A, 430, 669, doi: 10.1051/0004-6361:20041745

    work page doi:10.1051/0004-6361:20041745 2005

  6. [6]

    2019, title Stellar atmospheric parameters for 754 spectra from the X-shooter Spectral Library , , 627, A138, 10.1051/0004-6361/201834273

    Arentsen, A., Prugniel, P., Gonneau, A., et al. 2019, A&A, 627, A138, doi: 10.1051/0004-6361/201834273

    work page doi:10.1051/0004-6361/201834273 2019

  7. [7]

    The Chemical Composition of the Sun

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f...

    work page doi:10.1146/annurev.astro.46.060407.145222 2009

  8. [8]

    Models of the Solar Chromosphere and Transition Region from SUMER and HRTS Observations: Formation of the Extreme‐Ultraviolet Spectrum of Hydrogen, Carbon, and Oxygen

    Avrett, E. H., & Loeser, R. 2008, ApJS, 175, 229, doi: 10.1086/523671

    work page doi:10.1086/523671 2008

Show all 72 references
  1. [9]

    L., Donahue, R

    Baliunas, S. L., Donahue, R. A., Soon, W. H., et al. 1995, ApJ, 438, 269, doi: 10.1086/175072

    1995 doi

  2. [10]

    2011, ApJ, 733, 65, doi: 10.1088/0004-637X/733/1/65 Bjørgen, J

    Marois, C. 2011, ApJ, 733, 65, doi: 10.1088/0004-637X/733/1/65 Bjørgen, J. P., Sukhorukov, A. V., Leenaarts, J., et al. 2018, A&A, 611, A62, doi: 10.1051/0004-6361/201731926

    2011 doi

  3. [11]

    2019, MNRAS, 486, 2075, doi: 10.1093/mnras/stz549

    Blanco-Cuaresma, S. 2019, MNRAS, 486, 2075, doi: 10.1093/mnras/stz549

    2019 doi

  4. [12]

    2014a, A&A, 569, A111, doi: 10.1051/0004-6361/201423945

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

    doi

  5. [13]

    2014b, A&A, 566, A98, doi: 10.1051/0004-6361/201323153 Bus` a, I., Aznar Cuadrado, R., Terranegra, L., Andretta, V., & Gomez, M

    Blanco-Cuaresma, S., Soubiran, C., Jofr´ e, P., & Heiter, U. 2014b, A&A, 566, A98, doi: 10.1051/0004-6361/201323153 Bus` a, I., Aznar Cuadrado, R., Terranegra, L., Andretta, V., & Gomez, M. T. 2007, A&A, 466, 1089, doi: 10.1051/0004-6361:20065588

    2007 doi

  6. [14]

    Castelli, F., & Kurucz, R. L. 2003, in IAU Symposium, Vol. 210, Modelling of Stellar Atmospheres, ed. N. Piskunov, W. W. Weiss, & D. F. Gray, A20, doi: 10.48550/arXiv.astro-ph/0405087

    2003 doi

  7. [15]

    2009, A&A, 503, 577, doi: 10.1051/0004-6361/200811595

    Uitenbroek, H. 2009, A&A, 503, 577, doi: 10.1051/0004-6361/200811595

    2009 doi

  8. [16]

    Ciddor, P. E. 1996, ApOpt, 35, 1566, doi: 10.1364/AO.35.001566

    1996 doi

  9. [17]

    2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003 de Grijs, R., & Kamath, D

    Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003 de Grijs, R., & Kamath, D. 2021, Universe, 7, 440, doi: 10.3390/universe7110440

    2012 doi

  10. [18]

    K., Vaughan, A

    Duncan, D. K., Vaughan, A. H., Wilson, O. C., et al. 1991, ApJS, 76, 383, doi: 10.1086/191572

    1991 doi

  11. [19]

    M., Linsky, J

    Fontenla, J. M., Linsky, J. L., Garrison, J., et al. 2016, ApJ, 830, 154, doi: 10.3847/0004-637X/830/2/154 Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et a...

    2016 doi

  12. [20]

    Gray, D. F. 2008, The Observation and Analysis of Stellar Photospheres

    2008

  13. [21]

    O., & Corbally, C

    Gray, R. O., & Corbally, C. J. 1994, AJ, 107, 742, doi: 10.1086/116893

    1994 doi

  14. [22]

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

    2007 doi

  15. [23]

    2008, A&A, 486, 951, doi: 10.1051/0004-6361:200809724

    Gustafsson, B., Edvardsson, B., Eriksson, K., et al. 2008, A&A, 486, 951, doi: 10.1051/0004-6361:200809724

    2008 doi

  16. [24]

    C., Lockwood, G

    Hall, J. C., Lockwood, G. W., & Skiff, B. A. 2007, AJ, 133, 862, doi: 10.1086/510356

    2007 doi

  17. [25]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  18. [26]

    H., Barman, T., Baron, E., Aufdenberg, J

    Hauschildt, P. H., Barman, T., Baron, E., Aufdenberg, J. P., & Schweitzer, A. 2025, A&A, 698, A47, doi: 10.1051/0004-6361/202554171

    2025 doi

  19. [27]

    H., & Baron, E

    Hauschildt, P. H., & Baron, E. 1999, Journal of Computational and Applied Mathematics, 109, 41, doi: 10.48550/arXiv.astro-ph/9808182

    1999 doi

  20. [28]

    R., Bovy, J., Holtzman, J

    Hayden, M. R., Bovy, J., Holtzman, J. A., et al. 2015, ApJ, 808, 132, doi: 10.1088/0004-637X/808/2/132

    2015 doi

  21. [29]

    2015, PhyS, 90, 054010, doi: 10.1088/0031-8949/90/5/054010

    Heiter, U., Lind, K., Asplund, M., et al. 2015, PhyS, 90, 054010, doi: 10.1088/0031-8949/90/5/054010

    2015 doi

  22. [30]

    2022, MNRAS, 509, 4308, doi: 10.1093/mnras/stab3263 22

    Hill, L., Thomas, D., Maraston, C., et al. 2022, MNRAS, 509, 4308, doi: 10.1093/mnras/stab3263 22

    2022 doi

  23. [31]

    2024, ApJS, 272, 6, doi: 10.3847/1538-4365/ad33bc

    Huang, X., He, Y., Bai, Z., et al. 2024, ApJS, 272, 6, doi: 10.3847/1538-4365/ad33bc

    2024 doi

  24. [32]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  25. [33]

    O., Wende-von Berg, S., Dreizler, S., et al

    Husser, T. O., Wende-von Berg, S., Dreizler, S., et al. 2013, A&A, 553, A6, doi: 10.1051/0004-6361/201219058

    2013 doi

  26. [34]

    A., Yan, R., et al

    Imig, J., Holtzman, J. A., Yan, R., et al. 2022, AJ, 163, 56, doi: 10.3847/1538-3881/ac3ca7 J¨ onsson, H., Holtzman, J. A., Allende Prieto, C., et al. 2020, AJ, 160, 120, doi: 10.3847/1538-3881/aba592

    2022 doi

  27. [35]

    2021, MNRAS, 501, 5176, doi: 10.1093/mnras/staa3996

    Khoperskov, S., Haywood, M., Snaith, O., et al. 2021, MNRAS, 501, 5176, doi: 10.1093/mnras/staa3996

    2021 doi

  28. [36]

    Kordopatis, G., Wyse, R. F. G., Gilmore, G., et al. 2015, A&A, 582, A122, doi: 10.1051/0004-6361/201526258

    2015 doi

  29. [37]

    L., & VAMDC Collaboration

    Kupka, F., Dubernet, M. L., & VAMDC Collaboration. 2011, Baltic Astronomy, 20, 503, doi: 10.1515/astro-2017-0328 Lan¸ con, A., Gonneau, A., Verro, K., et al. 2021, A&A, 649, A97, doi: 10.1051/0004-6361/202039371

    2011 doi

  30. [38]

    D., Kupka, F., Ford, H

    Landstreet, J. D., Kupka, F., Ford, H. A., et al. 2009, A&A, 503, 973, doi: 10.1051/0004-6361/200912083

    2009 doi

  31. [39]

    C., Brugaletta, E., Fr´ emat, Y., et al

    Lanzafame, A. C., Brugaletta, E., Fr´ emat, Y., et al. 2023, A&A, 674, A30, doi: 10.1051/0004-6361/202244156

    2023 doi

  32. [40]

    2022, A&A, 668, A21, doi: 10.1051/0004-6361/202243701

    Lazarz, D., Yan, R., Wilhelm, R., et al. 2022, A&A, 668, A21, doi: 10.1051/0004-6361/202243701

    2022 doi

  33. [41]

    Linsky, J. L. 2017, ARA&A, 55, 159, doi: 10.1146/annurev-astro-091916-055327

    2017 doi

  34. [42]

    Kelch, W. L. 1979a, ApJS, 41, 481, doi: 10.1086/190627

    doi

  35. [43]

    L., Worden, S

    Linsky, J. L., Worden, S. P., McClintock, W., & Robertson, R. M. 1979b, ApJS, 41, 47, doi: 10.1086/190607

    doi

  36. [44]

    2026, The Astronomical Journal, 171, 256, doi: 10.3847/1538-3881/ae4ebe

    Liu, Q., Bai, Z., Zhou, M., et al. 2026, The Astronomical Journal, 171, 256, doi: 10.3847/1538-3881/ae4ebe

    2026 doi

  37. [45]

    L., Zhao, Y

    Luo, A. L., Zhao, Y. H., Zhao, G., & et al. 2019, VizieR Online Data Catalog: LAMOST DR5 catalogs (Luo+, 2019), VizieR On-line Data Catalog: V/164. Originally published in: 2019RAA..in.prep..L

    2019

  38. [46]

    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

  39. [47]

    J., & Przybilla, N

    Mashonkina, L., Korn, A. J., & Przybilla, N. 2007, A&A, 461, 261, doi: 10.1051/0004-6361:20065999

    2007 doi

  40. [48]

    Mittag, M., Schmitt, J. H. M. M., & Schr¨ oder, K. P. 2013, A&A, 549, A117, doi: 10.1051/0004-6361/201219868

    2013 doi

  41. [49]

    2011, A&A, 528, A44, doi: 10.1051/0004-6361/201015814

    Mucciarelli, A. 2011, A&A, 528, A44, doi: 10.1051/0004-6361/201015814

    2011 doi

  42. [50]

    W., Hartmann, L

    Noyes, R. W., Hartmann, L. W., Baliunas, S. L., Duncan, D. K., & Vaughan, A. H. 1984, ApJ, 279, 763, doi: 10.1086/161945

    1984 doi

  43. [51]

    1992, A&A, 266, 347

    Pasquini, L. 1992, A&A, 266, 347

    1992

  44. [52]

    2012, Turbospectrum: Code for spectral synthesis, Astrophysics Source Code Library, record ascl:1205.004

    Plez, B. 2012, Turbospectrum: Code for spectral synthesis, Astrophysics Source Code Library, record ascl:1205.004

    2012

  45. [53]

    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

  46. [54]

    2012, in SDO-4: Dynamics and Energetics of the Coupled Solar Atmosphere

    Rutten, R. 2012, in SDO-4: Dynamics and Energetics of the Coupled Solar Atmosphere. The Synergy Between State-of-the-Art Observations and Numerical Simulations, 110

    2012

  47. [55]

    L., et al

    Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, PhyS, 90, 054005, doi: 10.1088/0031-8949/90/5/054005

    2015 doi

  48. [56]

    Palicio, P. A. 2023, A&A, 672, A166, doi: 10.1051/0004-6361/202245606

    2023 doi

  49. [57]

    Sbordone, L., Bonifacio, P., Castelli, F., & Kurucz, R. L. 2004, Memorie della Societa Astronomica Italiana Supplementi, 5, 93, doi: 10.48550/arXiv.astro-ph/0406268

    2004 doi

  50. [58]

    2012, MOOG: LTE line analysis and spectrum synthesis, Astrophysics Source Code Library, record ascl:1202.009

    Sneden, C., Bean, J., Ivans, I., Lucatello, S., & Sobeck, J. 2012, MOOG: LTE line analysis and spectrum synthesis, Astrophysics Source Code Library, record ascl:1202.009

    2012

  51. [59]

    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

  52. [60]

    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

  53. [61]

    G., Fekel, F

    Strassmeier, K. G., Fekel, F. C., Bopp, B. W., Dempsey, R. C., & Henry, G. W. 1990, ApJS, 72, 191, doi: 10.1086/191414

    1990 doi

  54. [62]

    2006, ApJ, 639, 516, doi: 10.1086/499220

    Uitenbroek, H. 2006, ApJ, 639, 516, doi: 10.1086/499220

    2006 doi

  55. [63]

    A., & Piskunov, N

    Valenti, J. A., & Piskunov, N. 1996, A&AS, 118, 595

    1996

  56. [64]

    E., Avrett, E

    Vernazza, J. E., Avrett, E. H., & Loeser, R. 1981, ApJS, 45, 635, doi: 10.1086/190731

    1981 doi

  57. [65]

    2011, A&A, 536, A105, doi: 10.1051/0004-6361/201117752

    Vernet, J., Dekker, H., D’Odorico, S., et al. 2011, A&A, 536, A105, doi: 10.1051/0004-6361/201117752

    2011 doi

  58. [66]

    C., Peletier, R

    Verro, K., Trager, S. C., Peletier, R. F., et al. 2022, A&A, 660, A34, doi: 10.1051/0004-6361/202142388

    2022 doi

  59. [67]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  60. [68]

    H., Livingston, W

    Wallace, L., Hinkle, K. H., Livingston, W. C., & Davis, S. P. 2011, ApJS, 195, 6, doi: 10.1088/0067-0049/195/1/6

    2011 doi

  61. [69]

    Wilson, O. C. 1968, ApJ, 153, 221, doi: 10.1086/149652

    1968 doi

  62. [70]

    2019, ApJ, 883, 175, doi: 10.3847/1538-4357/ab3ebc

    Yan, R., Chen, Y., Lazarz, D., et al. 2019, ApJ, 883, 175, doi: 10.3847/1538-4357/ab3ebc

    2019 doi

  63. [71]

    2022, ApJS, 263, 12, doi: 10.3847/1538-4365/ac9406

    Zhang, W., Zhang, J., He, H., et al. 2022, ApJS, 263, 12, doi: 10.3847/1538-4365/ac9406

    2022 doi

  64. [72]

    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

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