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REVIEW 4 major objections 7 minor 107 references

Stellar atmospheric parameters and chemical abundances of about 5 million stars from S-PLUS multi-band photometry

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A photometric survey can stand in for spectroscopy to measure the chemistry of about five million stars.

desk verdict A genuinely useful S-PLUS stellar parameter catalog with credible Teff/logg/[Fe/H] estimates, but the headline claims of 'reliable' abundances for Li, O, Cu, and Si overstate what the filters and validation actually support. read the letter →

arxiv 2411.18748 v1 pith:LIIVMUKI submitted 2024-11-27 astro-ph.GA astro-ph.IMastro-ph.SR

C. E. Ferreira Lopes , L. A. Gutiérrez-Soto , V. S. Ferreira Alberice , N. Monsalves , D. Hazarika , M. Catelan , V. M. Placco , G. Limberg
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F. Almeida-Fernandes H. D. Perottoni A. V. Smith Castelli S. Akras J. Alonso-García V. Cordeiro M. Jaque Arancibia S. Daflon B. Dias D. R. Gonçalves E. Machado-Pereira A. R. Lopes C. R. Bom R. C. Thom de Souza N. G. de Isídio A. Alvarez-Candal M. E. De Rossi C. J. Bonatto B. Cubillos Palma M. Borges Fernandes P. K. Humire G. B. Oliveira Schwarz W. Schoenell A. Kanaan C. Mendes de Oliveira
This is my paper · ORCID
classification astro-ph.GAastro-ph.IMastro-ph.SR
keywords S-PLUSsurveyphotometricstellarparameterschemicalabundancesmachinelearningcost-sensitiveneuralnetworkrandomforestGalacticpopulationsnarrowbandphotometry
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that the 66 colors formed from the 12 S-PLUS photometric bands carry enough information to estimate effective temperature, surface gravity, iron abundance, and several elemental abundance ratios for roughly five million Milky Way stars, without taking a single spectrum. The models, cost-sensitive neural networks and random forests trained on overlapping APOGEE, GALAH, and LAMOST stars, recover $T_{\rm eff}$, $\log g$, $[\mathrm{Fe/H}]$, $[\alpha/\mathrm{Fe}]$, $[\mathrm{Al/Fe}]$, $[\mathrm{C/Fe}]$, $[\mathrm{Li/Fe}]$, and $[\mathrm{Mg/Fe}]$ with goodness-of-fit above 60%, once the 66 colors and estimated $T_{\rm eff}$ and $\log g$ are used as inputs. If the estimates hold, the catalog turns narrowband photometry into a spectroscopic-scale chemical map of the disk, enabling population and chemo-dynamical studies that currently require high-resolution spectroscopy. The authors validate the results against star-cluster metallicities, TESS input catalog data, and J-PLUS predictions, and flag stars whose features fall outside the training ranges.

What carries the argument

The load-bearing object is a cost-sensitive neural network: a six-layer feed-forward network with 1664 neurons that takes the 66 S-PLUS colors as input and weights rare training cases more heavily, so that under-represented parameter values are not ignored. In the preferred configuration, the network first estimates $T_{\rm eff}$ and $\log g$ from the 66 colors, then uses those estimates as additional input columns (68 features total) to predict each abundance ratio; a random forest trained on the same inputs serves as a cross-check, and an r2-based goodness-of-fit decides which parameters are kept. A feature-flag system records what fraction of a star's input features lie inside the training-set limits, since out-of-range colors are clipped to the training minimum or maximum.

What would settle it

Compare the catalog's $[\mathrm{Fe/H}]$ values for a few hundred stars with spectroscopic $[\mathrm{Fe/H}] < -2$ that were not used in training; if the photometric values show systematic offsets comparable to the roughly 0.3 dex excess already seen for M30, the extrapolation claim would fail.

Watch

Extended reading notes

Core claim

On its own terms, the discovery is that the narrowband S-PLUS system is not just a stellar classifier but a chemical-abundance probe: using the twelve observed magnitudes to build all pairwise colors, and training a cost-sensitive neural network on spectroscopic labels from APOGEE, GALAH, and LAMOST, the paper estimates stellar atmospheric parameters and abundance ratios for about 140,000 giants and 4.9 million dwarfs. The neural network consistently beats the random forest, and feeding $T_{\rm eff}$ and $\log g$ back into the network as extra features improves accuracy by about 3%, with the largest gains for $[\mathrm{Fe/H}]$ and $[\mathrm{Mg/Fe}]$. Only parameters with goodness-of-fit above 50% across all approaches are kept, and the most reliable ones, including $T_{\rm eff}$, $\log g$, $[\mathrm{Fe/H}]$, $[\alpha/\mathrm{Fe}]$, $[\mathrm{Al/Fe}]$, $[\mathrm{C/Fe}]$, $[\mathrm{Li/Fe}]$, and $[\mathrm{Mg/Fe}]$, exceed 60%; $[\mathrm{Cu/Fe}]$, $[\mathrm{O/Fe}]$, and $[\mathrm{Si/Fe}]$ are released with cautionary flags. The paper further shows that the estimates reproduce known Milky Way trends, such as the radial iron gradient and the bimodal $[\mathrm{Mg/Fe}]$-$[\mathrm{Fe/H}]$ distribution, and can be used to select star-cluster members by metallicity.

Load-bearing premise

The models are trained on a few thousand stars per survey and almost no stars below $[\mathrm{Fe/H}] = -2$, yet they are applied to about five million S-PLUS stars whose out-of-range colors are clamped to the training limits; the assumption is that this extrapolation still produces meaningful values.

Editorial extensions

If this is right

  • The released catalog gives roughly five million stars with $T_{\rm eff}$, $\log g$, $[\mathrm{Fe/H}]$, $[\alpha/\mathrm{Fe}]$, $[\mathrm{Al/Fe}]$, $[\mathrm{C/Fe}]$, $[\mathrm{Li/Fe}]$, and $[\mathrm{Mg/Fe}]$, a sample size competitive with large spectroscopic surveys but obtained from photometry.
  • The estimated metallicities can identify likely star-cluster members and reject interlopers, as demonstrated on globular and open clusters.
  • The S-PLUS-based estimates reproduce the Milky Way's radial metallicity gradient and the bimodal $[\mathrm{Mg/Fe}]$-$[\mathrm{Fe/H}]$ distribution, so the catalog can support chemo-dynamical studies of the disk.
  • The same trained models are ready to apply to S-PLUS IDR5 and the Ultra-Short Survey, roughly doubling the data volume and extending the sky coverage without new spectroscopy.

Reading between the lines

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

  • A reader should treat the metal-poor tail ($[\mathrm{Fe/H}]$ below about -2) as the untested edge: the training sets contain only tens of stars there, and the paper's own M30 comparison shows a roughly 0.3 dex overestimate, so catalog values in that regime are best used as candidates, not measurements.
  • Because the narrowband filters J0378, J0395, J0410, and J0430 plus the u-band dominate feature importance, the method suggests that even a few well-chosen medium bands can carry most of the chemical-abundance information; a future survey could optimize filter placement around those features.
  • Combining this catalog with Gaia astrometry should make it possible to separate thin-disk and thick-disk populations on a sample of millions of stars, which the paper notes as a forthcoming application rather than a completed one.
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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 / 7 minor

Summary. This paper presents photometric estimates of stellar atmospheric parameters (Teff, log g, [Fe/H]) and elemental abundance ratios for ~5 million sources in S-PLUS DR4, using cost-sensitive neural networks and random forests trained on spectroscopic labels from LAMOST, APOGEE, and GALAH. The input representation is the 66 S-PLUS colors, and three strategies (colors alone; colors plus Teff; colors plus Teff and log g) are compared. Parameters with holdout goodness-of-fit above 50% are retained, feature-importance analyses are presented, and a flagged catalog is released at CDS. Validation is attempted through star-cluster memberships, the TESS Input Catalog, and a comparison with J-PLUS predictions and GALAH spectroscopy for 186 common dwarfs and 8 giants. The central claim, stated in the abstract and conclusions, is that 'reliable estimates' of Teff, log g, [Fe/H], [alpha/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] are obtained for roughly 5 million stars with goodness-of-fit above 60%, with additional, less accurate estimates for [Cu/Fe], [O/Fe], and [Si/Fe].

Significance. If the headline claim were fully supported, the catalog would be a valuable community resource, extending abundance estimation to a sample an order of magnitude larger than current spectroscopic surveys and providing a framework reusable for S-PLUS DR5 and the Ultra-Short Survey. The paper has real strengths: the use of three independent training surveys, the deliberate inclusion of abundances without corresponding S-PLUS features as a spurious-correlation probe, a transparent flag system (FF, Flag 00-02, FlagTIC), and a public catalog. The physical coherence checks, such as the [Mg/Fe]-[Fe/H] bimodality and the Galactic metallicity gradients, are encouraging. However, the evidence as presented does not support the strongest claim. The test-set R^2 statistics are computed within the training distribution; the metal-poor tail of the training sets is extremely sparse (Table 2); the [Li/Fe] claim conflicts with the paper's own caution about abundances lacking S-PLUS features; and the principal external validations are weakened by selection on the predicted quantity (Section 4.3) and by overlap or methodological kinship of the comparison catalogs (Sections 4.4-4.5).

major comments (4)
  1. [Abstract; Sections 2 and 4.1; Tables 2-3] The claim in the abstract and conclusions that 'reliable estimates' are obtained for approximately 5 million stars is not demonstrated for the parts of the catalog outside the training distribution. The models are trained on a few thousand to a few tens of thousands of cross-matched sources per survey (Section 2 gives 2,877, 5,916, and 573 sources for APOGEE, GALAH, and LAMOST after cross-matching, while the per-parameter counts in Table 1 are several times larger), and Table 2 shows the metal-poor tail is extremely sparse: 66, 121, and 9 stars for -3 < [Fe/H] < -2, and 14, 34, and 4 stars below -3. Section 4.1 then applies the models to about 5 million sources, clamping features outside the training range to the minimum or maximum and stating that this 'can introduce biases'; Table 3 shows that a non-negligible fraction of dwarf stars have at least one feature outside the training limits (for example, about 32% of the LAMOST-based dwarf Teff sample falls below the 100% column). Because the reported goodness-of-fit above 60% is a test-set statistic drawn from the training distribution, it does not quantify error under this distribution shift, and no out-of-distribution calibration is provided. The reliability statement should be conditioned on the FF flag and on the parameter ranges, or an explicit extrapolation test (for example, against a metal-poor spectroscopic sample) should be supplied.
  2. [Abstract; Section 2; Section 4 flag definitions; Table 1] The abstract and Section 5 list [Li/Fe] among the reliable estimates with goodness-of-fit above 60%, but no S-PLUS filter is centered on a lithium line (Section 1 lists the narrowband features as [O II], Ca H+K, H-delta, CH G-band, Mgb triplet, H-alpha, and Ca triplet), and Section 2 explicitly warns: 'It is crucial to exercise caution when interpreting elemental abundances that lack corresponding features in S-PLUS filters.' A high test-set R^2 for [Li/Fe] is expected even if the model merely reproduces the known correlations of lithium with Teff, log g, and [Fe/H], which the S-PLUS colors do constrain; the R^2-based Flag 00 therefore does not establish that the lithium abundance itself is recovered. To support the claim, the authors should compare the photometric [Li/Fe] predictions against a null model that predicts [Li/Fe] from Teff, log g, and [Fe/H] alone (showing that the colors add predictive power beyond these parameters), or validate against lithium measurements from a spectroscopic sample, or explicitly reclassify [Li/Fe] as a lower-reliability, correlation-based estimate in the abstract, conclusions, and flag definitions.
  3. [Section 4.3; Figure 10] The star-cluster validation of [Fe/H] selects stars using the quantity being validated: 'we focus only on stars whose metallicities are close to those reported in the literature, applying an error tolerance of 0.2 x (1 + |[Fe/H]_Literature|).' Because the photometric metallicity is part of this preselection, the agreement shown in Figure 10 is inflated and the procedure is partially circular; the authors acknowledge that the criterion 'introduced some bias into the results.' Furthermore, the most metal-poor cluster tested, NGC 7099 with [Fe/H] approximately -2.29, is overestimated by about 0.3 dex, which is precisely the regime where Table 2 shows the training data to be thinnest. The cluster validation should be recomputed without the metallicity preselection (relying on membership probability and photometric quality cuts alone), and the metal-poor offset should be incorporated into the reliability statements.
  4. [Sections 4.4-4.5] The two remaining external checks do not provide fully independent confirmation of accuracy. Section 4.4 compares with the TESS Input Catalog and concedes that some of its entries 'may overlap with our training set,' and the [M/H]-[Fe/H] relation of Equation (2) is then calibrated on that same comparison sample (Table 4), making the exercise a consistency check rather than an accuracy test. Section 4.5 compares S-PLUS predictions with J-PLUS predictions produced by the same cost-sensitive neural-network methodology (Yang et al. 2022) for only 186 dwarfs and 8 giants; agreement between two applications of the same method is not an independent test of the abundance scale. The authors should either add a genuinely independent validation set (for example, high-resolution abundances for stars excluded from training) or soften the abstract claim that star clusters, TESS, and J-PLUS data 'confirmed the robustness of our methodology.'
minor comments (7)
  1. [Section 2 vs Table 1] The text states that the cross-match yielded about 2,877, 5,916, and 573 sources for APOGEE, GALAH, and LAMOST, while Table 1 reports per-parameter counts that are several times larger (for example, 8,885 APOGEE dwarfs and 5,500 APOGEE giants for Teff); the relationship between these numbers should be clarified, as it determines the effective training-set sizes quoted by future users.
  2. [Section 4.2] The sentence 'the lack of correlation between Teff and metallicity is not expected' presumably should read 'is expected'; as written, it states the opposite of the argument developed in the following sentences.
  3. [Section 4.3] The sentence 'with sigma values smaller than -0.5 dex' appears to contain a sign error; the context indicates the intended statement is sigma values smaller than 0.5 dex.
  4. [Section 4.5] The phrase 'S-PLUS surplases JPLUS' contains a typo and should read 'surpasses.'
  5. [Figure 3 caption] The caption contains the typo 'trainning' and should read 'training.'
  6. [Section 4.1; Table 3] The statement that 'about 75% of dwarf stars fall within the constraints of the training sets' does not match the 100% column of Table 3 for any single row, where the fraction ranges from roughly 68% (LAMOST) to roughly 89% (APOGEE and GALAH Teff); the aggregation used for this number should be stated.
  7. [Appendices A-G] Appendices A through G are referenced as 'see link,' so the goodness-of-fit tables, feature-importance plots, catalog description, and cluster comparison tables are not available in the manuscript itself; these materials should be included or clearly referenced to a stable location in the revised version.

Circularity Check

1 steps flagged · score 3.0 of 10

Cluster validation is self-selected by construction, but the core color-to-label mapping is an external supervised fit and is not circular.

  1. self definitional [Section 4.3, Star clusters]
    "To analyze how closely the photometric metallicities align with the spectroscopic values, we focus only on stars whose metallicities are close to those reported in the literature, applying an error tolerance of 0.2×(1+|[Fe/H]Literature|)."

    The cluster test is the paper's principal external validation (Section 4.3, Fig. 10), but the sample used for the comparison is defined by requiring the photometric [Fe/H] being tested to lie within 0.2×(1+|[Fe/H]_lit|) of the literature value. Stars whose estimates disagree by more than this tolerance are excluded before the agreement statistics are computed, so the reported agreement and the small sigma values are partly guaranteed by the selection rule rather than by the model. The paper itself concedes 'this introduced some bias into the results'. This makes the validation loop self-referential, although it does not make the catalog predictions themselves circular because the training labels are external spectroscopic surveys.

full rationale

The paper's central derivation is a standard supervised regression: 66 S-PLUS colors (plus Teff/log g in approaches B/C) are mapped to spectroscopic labels from APOGEE, GALAH, and LAMOST by cost-sensitive neural networks and random forests. The target labels are external to S-PLUS, so the catalog values are not defined in terms of the outputs, and no equation in the paper reduces a predicted abundance to its fitting input. The retention threshold (goodness-of-fit > 50%) and the reported >60% figures are test-split statistics, which may overstate generalization to the ~5M stars with features outside the training ranges (Section 4.1, Table 3), but that is an extrapolation risk, not circularity. The paper's own cluster validation is partially circular: Section 4.3 selects cluster members by requiring the photometric metallicity under test to be within 0.2×(1+|[Fe/H]_lit|) of the literature value before computing agreement, so the agreement is partly by construction. The TIC comparison is also weakened by the admitted possible overlap with the training set (Section 4.4), and the J-PLUS comparison uses a catalog produced by the same NN methodology. None of these validation weaknesses feeds back into the derivation of the S-PLUS estimates, so the central claim retains independent content. Score 3.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The central derivation is a trained machine-learning model, so the free parameters are the architecture choices, thresholds, and fitted normalization/calibration coefficients. The axioms are the representativeness of the training data and the physical sensitivity of the colors to the labels. No new physical entities are postulated.

free parameters (6)
  • R^2 selection threshold = 50% retention, 60% for Flag 00
    Chosen by hand to filter parameters; this threshold determines which abundances are released and which flags are assigned.
  • NN architecture size = 6 layers, 1664 neurons
    Tuned on training data; the distribution of neurons across layers is not specified.
  • RF hyperparameters = not reported
    Tuned per parameter (number of trees, max depth, max features), values not given in the text.
  • Eq. (2) coefficients a and b = Table 4 values per training set and dwarf/giant
    Fitted to the S-PLUS versus TIC [M/H]-[Fe/H] comparison; used to compute FlagTIC.
  • Cluster member selection tolerance = 0.2*(1+|[Fe/H]_lit|)
    Chosen to select stars for cluster validation; introduces selection bias.
  • Signal-to-noise cuts = SNR>5 for S-PLUS, SNR>20 for training spectra
    Adopted to ensure data quality; affects sample composition.
assumptions (7)
  • domain assumption S-PLUS 12-band colors contain sufficient information to reconstruct Teff, log g, [Fe/H] and several abundance ratios.
    Central premise of the method, stated in Sections 1 and 2.
  • domain assumption Spectroscopic labels from APOGEE, GALAH, and LAMOST are accurate and mutually consistent enough to serve as training targets.
    The authors note offsets larger than 0.1 dex between surveys but train separate models, treating each survey's labels as ground truth.
  • domain assumption The training samples, after cross-match (thousands of stars), are representative of the ~5 million star S-PLUS sample.
    The paper extrapolates to stars outside the training parameter ranges, clamping out-of-range features to min/max (Section 4.1).
  • domain assumption Test-set R^2 on a 20% holdout from the same survey is a valid indicator of predictive reliability for new populations.
    Used to select parameters and assign flags (Section 4).
  • ad hoc to paper Abundances with no absorption feature in the S-PLUS filters can still be reliably estimated if R^2 is high.
    Applied to [Li/Fe], [O/Fe], [Cu/Fe], and [Si/Fe]; the physical basis is not established.
  • domain assumption Dwarf/giant classification based on Gaia log g and Teff using Thomas et al. (2019) criteria is correct for the full sample.
    Used to separate training and prediction (Section 2).
  • standard math Extinction corrections using SFD maps, Schlafly-Finkbeiner scaling, and SVO coefficients are adequate.
    Standard practice in photometric surveys (Section 2).

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Cite this review

Pith. "Pith review of Stellar atmospheric parameters and chemical abundances of about 5 million stars from S-PLUS multi-band photometry." pith.science (2026). https://pith.science/paper/LIIVMUKI

@misc{pith2026241118748,
  author       = {Pith},
  title        = {Pith review of: Stellar atmospheric parameters and chemical abundances of about 5 million stars from S-PLUS multi-band photometry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LIIVMUKI}},
  note         = {Machine review of arXiv:2411.18748}
}
read the original abstract

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts.

Figures

Figures reproduced from arXiv: 2411.18748 by the authors.

Figure 1
Figure 1. Sky footprints of S-PLUS DR4, APOGEE, GALAH DR3, and LAMOST MRS DR8 surveys across the celestial sphere. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. S-PLUS photometry and SDSS spectra of a dwarf star (top panel) and a giant star (bottom panel). The colored symbols [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Corner plot for the APOGEE, LAMOST, and GALAH training sets, that coincide with S-PLUS data, indicated in green, red, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Density plots illustrating the distribution of log [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Comparative analysis of RF and NN goodness-of-fit [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: The 10 primary features utilized for prediction are ar [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Results of the testing set for various astrophysical parameters ( [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Density plot depicting the pairwise distribution of di [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Spatial distribution of Milky Way giant stars and their various chemical abundances calculated in this study. In each plot, [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Comparison of metallicity ([Fe/H]) values from the liter￾ature (x-axis) with the estimations obtained in this work (y-axis) for the selected star clusters. 4.3. Star clusters We performed a crossmatch with star clusters to evaluate the accuracy of our derived paramete…
Figure 11
Figure 11. Figure 11: Sky plots for globular clusters NGC 104 (left panel) and NGC 3201 (right panel). The color bar in each plot indicates the [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Comparison of [M/H] vs. [Fe/H] for dwarf stars (top panels) and giant stars (bottom panels). The color gradient represents [α/M], while the dashed and solid grey lines depict the linear fits and one-to-one relationship, respectively. globular clusters are monometallic…
Figure 13
Figure 13. Figure 13: Empirical Cumulative Distribution Functions (ECDFs) comparing predicted [Fe/ [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: Comparison of the pairwise differences between the predicted values from the S-PLUS (this work), J-PLUS and spectro￾scopic values from GALAH for [Fe/H], Teff, and log g, for 186 dwarf stars. The dashed-dotted lines represent mean value of each distribution. The 1σ val…

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