pith. machine review for the scientific record. sign in

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

Recognition: unknown

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

Ganyu Li, Hailong Yuan, Haotong Zhang, Jingkun Zhao, Mengxin Wang, Mingjie Jian, Mingkuan Yang, Qian Liu, Xiaoting Fu, Xiaozhen Yang, Yiqiao Dong, Zhongrui Bai, Ziyue Jiang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 10:37 UTC · model grok-4.3

classification 🌌 astro-ph.SR
keywords Ca II infrared tripletchromospheric activitysynthetic template subtractionFGK starsresidual indicesLAMOSTNLTE effectsmicroturbulent velocity
0
0 comments X

The pith

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

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

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.

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.

What carries the argument

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

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.

Where Pith is reading between the lines

These are 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.

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.

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.

Figures

Figures reproduced from arXiv: 2604.14642 by Ganyu Li, Hailong Yuan, Haotong Zhang, Jingkun Zhao, Mengxin Wang, Mingjie Jian, Mingkuan Yang, Qian Liu, Xiaoting Fu, Xiaozhen Yang, Yiqiao Dong, Zhongrui Bai, Ziyue Jiang.

Figure 1
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 r… view at source ↗
Figure 2
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. 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 l… view at source ↗
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 parameters are listed in the panel titles, while the varying parameter in each panel is indicated by color. The adopted parameter steps are ∆Teff = 2… view at source ↗
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 APOGEE parameters in red and MaStar median parameters in black. Blue points in [Fe/H] show MaStar [Fe/H]cal. Solid black line: y = x; dashed… view at source ↗
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] values. The residuals show ∆R between two [Fe/H] values at log g = 4.4 and [α/Fe]= 0. also exhibits low dispersion for cluster members, indi… view at source ↗
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 are convolved with the instrumental LSF and the MaStar spectra with a Gaussian kernel. In each panel, the upper subpanel presents the measur… view at source ↗
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 corresponding median uncertainties. After incor￾porating all sources of uncertainty, we generated a full set of simulated observations and mea… view at source ↗
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 Teff panels, the red curve denotes the 5th-percentile relation. High-density regions are shown in yellow, and the median lines are plotted in … view at source ↗
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 line core remains slightly shallower than that 8540 8542 8544 8546 8548 8550 (Å) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized flux BT-Settl GSL NewEra A1… view at source ↗
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, τ500 (defined such that τ500 = 1 at h = 0), electron number density ne, temperature T, and microturbulent velocity Vmic. Line styles distingu… view at source ↗
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, while the lower panels display the cool, metal-poor star HD 190404; neither star exhibits obvious emission cores. The panel titles list the stel… view at source ↗
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 of synthetic spec￾tra, and finally assess how these differences affect the in￾ferred basal boundary and the interpretation of survey￾based a… view at source ↗
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 component is varied at a time. The solar abundances are adopted from Grevesse2007. thesized with SPECTRUM show relatively deeper line core and b… view at source ↗
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 that specific choice; the modified component is indicated in the upper-left corner of the first- and second-row panels. The bottom row shows t… view at source ↗
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 combinations of solar abundance scale and line list. Each curve corresponds to a different synthesis configuration. The A1 5th-percentile curve i… view at source ↗
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.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

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

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.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 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.
axioms (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.

pith-pipeline@v0.9.0 · 5589 in / 1426 out tokens · 21712 ms · 2026-05-10T10:37:05.482448+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

72 extracted references · 65 canonical work pages · 3 internal anchors

  1. [1]

    , keywords =

    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]

    2011, in Cool Stars 16, Vol

    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 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]

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

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

    work page internal anchor 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]

    , keywords =

    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]

    , keywords =

    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]

    H., & Loeser, R

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

    work page doi:10.1086/523671 2008

  9. [9]

    L., Donahue, R

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

    work page doi:10.1086/175072 1995

  10. [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

    work page doi:10.1088/0004-637x/733/1/65 2011

  11. [11]

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

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

    work page doi:10.1093/mnras/stz549 2019

  12. [12]

    2014, 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

    work page doi:10.1051/0004-6361/201423945

  13. [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

    work page doi:10.1051/0004-6361/201323153 2007

  14. [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

    work page internal anchor Pith review doi:10.48550/arxiv.astro-ph/0405087 2003

  15. [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

    work page doi:10.1051/0004-6361/200811595 2009

  16. [16]

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

    work page doi:10.1364/ao.35.001566 1996

  17. [17]

    2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003 D´ enes, H., Kilborn, V

    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

    work page doi:10.1088/1674-4527/12/9/003 2012

  18. [18]

    K., Vaughan, A

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

    work page doi:10.1086/191572 1991

  19. [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 al. 2023, A&A, 674, A1, doi: 10.1051/0004-6361/202243940 Garc´ ıa P´ erez, A. E.,...

    work page doi:10.3847/0004-637x/830/2/154 2016

  20. [20]

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

    2008

  21. [21]

    O., & Corbally, C

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

    work page doi:10.1086/116893 1994

  22. [22]

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

    work page doi:10.1007/s11214-007-9173-7 2007

  23. [23]

    G., Nordlund A ., Plez B., 2008, @doi [ ] 10.1051/0004-6361:200809724 , https://ui.adsabs.harvard.edu/abs/2008A&A...486..951G 486, 951

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

    work page doi:10.1051/0004-6361:200809724 2008

  24. [24]

    C., Lockwood, G

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

    work page doi:10.1086/510356 2007

  25. [25]

    Harris, 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

    work page doi:10.1038/s41586-020-2649-2 2020

  26. [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

    work page doi:10.1051/0004-6361/202554171 2025

  27. [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

    work page doi:10.48550/arxiv.astro-ph/9808182 1999

  28. [28]

    , keywords =

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

    work page doi:10.1088/0004-637x/808/2/132 2015

  29. [29]

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

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

    work page doi:10.1088/0031-8949/90/5/054010 2015

  30. [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

    work page doi:10.1093/mnras/stab3263 2022

  31. [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

    work page doi:10.3847/1538-4365/ad33bc 2024

  32. [32]

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

    work page doi:10.1109/mcse.2007.55 2007

  33. [33]

    2013, A&A, 553, A6, doi: 10.1051/0004-6361/201219058

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

    work page doi:10.1051/0004-6361/201219058 2013

  34. [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

    work page doi:10.3847/1538-3881/ac3ca7 2022

  35. [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

    work page doi:10.1093/mnras/staa3996 2021

  36. [36]

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

    work page doi:10.1051/0004-6361/201526258 2015

  37. [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

    work page doi:10.1515/astro-2017-0328 2011

  38. [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

    work page doi:10.1051/0004-6361/200912083 2009

  39. [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

    work page doi:10.1051/0004-6361/202244156 2023

  40. [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

    work page doi:10.1051/0004-6361/202243701 2022

  41. [41]

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

    work page doi:10.1146/annurev-astro-091916-055327 2017

  42. [42]

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

    work page doi:10.1086/190627

  43. [43]

    L., Worden, S

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

    work page doi:10.1086/190607

  44. [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

    work page doi:10.3847/1538-3881/ae4ebe 2026

  45. [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

  46. [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

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

  47. [47]

    A&A , keywords =

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

    work page doi:10.1051/0004-6361:20065999 2007

  48. [48]

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

    work page doi:10.1051/0004-6361/201219868 2013

  49. [49]

    , keywords =

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

    work page doi:10.1051/0004-6361/201015814 2011

  50. [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

    work page doi:10.1086/161945 1984

  51. [51]

    1992, A&A, 266, 347

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

    1992

  52. [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

  53. [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

    work page doi:10.1051/0004-6361/202243750 2023

  54. [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

  55. [55]

    , year = 2015, month = may, volume =

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

    work page doi:10.1088/0031-8949/90/5/054005 2015

  56. [56]

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

    work page doi:10.1051/0004-6361/202245606 2023

  57. [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

    work page internal anchor Pith review doi:10.48550/arxiv.astro-ph/0406268 2004

  58. [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

  59. [59]

    , keywords =

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

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

  60. [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

    work page doi:10.1051/0004-6361/201628497 2016

  61. [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

    work page doi:10.1086/191414 1990

  62. [62]

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

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

    work page doi:10.1086/499220 2006

  63. [63]

    A., & Piskunov, N

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

    1996

  64. [64]

    E., Avrett, E

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

    work page doi:10.1086/190731 1981

  65. [65]

    , keywords =

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

    work page doi:10.1051/0004-6361/201117752 2011

  66. [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

    work page doi:10.1051/0004-6361/202142388 2022

  67. [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

    work page doi:10.1038/s41592-019-0686-2 2020

  68. [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

    work page doi:10.1088/0067-0049/195/1/6 2011

  69. [69]

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

    work page doi:10.1086/149652 1968

  70. [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

    work page doi:10.3847/1538-4357/ab3ebc 2019

  71. [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

    work page doi:10.3847/1538-4365/ac9406 2022

  72. [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

    work page doi:10.1088/1674-4527/12/7/002 2012