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The T-GEX project. I. Stealth UV-bright Sun-like stars in the Galaxy as candidate contributors to the UV upturn

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

Pith's one-line read Sun-like main-sequence stars with strong ultraviolet excess may be a significant, overlooked contributor to the UV upturn of old galaxies.

desk verdict A candid, well-scoped feasibility study whose central stellar claim is still hostage to unresolved binaries; worth refereeing for the catalogue, not yet for the galaxy scaling. read the letter →

arxiv 2607.27324 v1 pith:N7E3PGM4 submitted 2026-07-29 astro-ph.SR astro-ph.GA

classification astro-ph.SRastro-ph.GA
keywords UVupturnFGKstarsmain-sequenceturn-offultravioletexcessstellarpopulationsquiescentgalaxieschromosphericactivityT-GEXcatalogue
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 assembles a small catalogue of 37 apparently ordinary Sun-like (FGK) main-sequence turn-off stars and measures their ultraviolet emission against an empirical locus for normal FGK stars. It finds that roughly two-thirds of them are ultraviolet-abnormal, with the strongest excess concentrated in the coolest, alpha-enhanced, sub-solar-metallicity stars. It then asks what would happen if such stars are common and long-lived in old galaxies, and shows by an IMF-based scaling that a median template reproduces at most about 20 percent of the UV output of three representative UV-upturn galaxies, while the bluest template could match most or all of it if roughly 10–15 percent of surviving FGK stars are UV-bright. The claim is that ordinary Sun-like stars are a viable, previously overlooked channel for the UV upturn, not a replacement for the usual hot-star and binary channels.

What carries the argument

The load-bearing machinery is the T-GEX catalogue itself: 37 stars with high-resolution optical spectroscopy, astrometry, ultraviolet and infrared photometry, all passed through quality cuts against binarity and spectral peculiarity. The analysis has three instruments: (1) the empirical UV-normal locus for FGK stars, used to classify stars as UV-normal or UV-abnormal; (2) a three-component Gaussian mixture model in the ultraviolet-optical colour plane, which defines the cool, hot, and intermediate groups; and (3) an IMF-based scaling calculation that converts per-star ultraviolet luminosities into a galaxy-level fraction, using a broken power-law IMF, the main-sequence lifetime relation, the

What would settle it

Multi-epoch radial velocities plus high-angular-resolution imaging (or UV spectroscopy) of the Group 1 stars: if a substantial fraction resolve into white-dwarf+FGK binaries or the FUV source separates from the optical star, the single-star interpretation collapses and the galaxy scaling must be renormalized to the true single-star duty cycle.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that a large fraction of cool FGK main-sequence stars with no obvious astrometric or spectroscopic peculiarities have ultraviolet fluxes far above the empirical UV-normal relation, including a group whose FUV−NUV colours overlap those of UV-upturn galaxies. A three-component Gaussian mixture in the FUV−NUV versus NUV−G plane splits the sample into a cool strongly UV-excess group, a hotter UV-normal group, and an intermediate UV-excess group, and these groups differ in temperature, metallicity, alpha-enhancement, and age. Feeding empirical per-star UV luminosities through a Kroupa-like IMF scaling for three UV-upturn galaxies, the authors sho

Load-bearing premise

The load-bearing premise is that the measured ultraviolet excess is intrinsic to the FGK stars themselves, or at least representative of single FGK stars in old galaxies, rather than produced by unresolved white-dwarf companions, post-interaction binaries, or chance alignments inside the ultraviolet beam; the paper states explicitly that close, faint, or UV-dominant companions cannot be excluded.

Editorial extensions

If this is right

  • If two-thirds of field FGK/MSTO stars are UV-abnormal, stellar population models of old systems should include a cool main-sequence UV component, not only hot evolved stars, binaries, and remnants.
  • With typical (median) UV-excess luminosities, UV-bright FGK stars can account for at most ~20% of the UV output in UV-upturn galaxies, so they are a sub-dominant but non-negligible channel.
  • If the most extreme UV-bright tail exists in galaxy populations, a small fraction (~10–15%) of surviving FGK stars could match most or all of the observed FUV and NUV emission, making the UV upturn strongly sensitive to the extreme tail.
  • The UV-abnormal groups have distinct chemical patterns (cool group alpha-enhanced and sub-solar; intermediate group highest metallicity), suggesting UV excess is tied to particular stellar populations rather than being random.
  • Within the available spectra, strong H-alpha core emission appears only among hotter UV-normal stars, so chromospheric activity is not established as the source of the UV excess.

Reading between the lines

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

  • If future observations confirm that the UV excess is intrinsic to single stars, the UV upturn in old galaxies may correlate with alpha-enhancement and sub-solar metallicity, because that is where Group 1 sits; this is a prediction the authors do not make explicitly.
  • The bluest template's formal young age is flagged as degenerate; an editorial reading is that the duty cycle and lifetime of the UV-bright phase, not just its instantaneous fraction, is the key unknown controlling the galaxy-level signal.
  • A volume-complete census of FGK stars with ultraviolet photometry could measure the true UV-bright fraction directly and test whether the 37-star sample's ~2/3 abnormal rate survives selection correction.
  • Galaxies with recent or ongoing low-level star formation could have a young component of UV-bright FGK stars; the template scaling does not separate these, so mixing the two would require careful SED modelling.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents the T-GEX catalogue, 37 Galactic FGK/MSTO field stars with Gaia-ESO spectroscopy, Gaia DR3 astrometry, GALEX FUV/NUV photometry, and 2MASS/AllWISE infrared data. The authors classify the stars relative to the empirical FGK UV-normal locus of Smith et al. (2014), apply a three-component Gaussian Mixture Model in the FUV-NUV vs. NUV-G plane, measure H-alpha activity diagnostics for the 24 stars with coverage, and carry out an IMF-based scaling calculation to estimate how much of the UV emission of three GAMA UV-upturn galaxies could be matched by UV-bright FGK stars. The central claims are that about two-thirds of the sample are UV-abnormal, that the UV-excess population is clustered into a cool extreme group (G1), a hotter UV-normal group (G2), and an intermediate group (G3), and that if such UV-bright FGK stars are common and long-lived they could contribute substantially to the UV upturn, with the bluest Group 1 template matching most or all of the NUV/FUV emission at an assumed 10-15% FGK fraction. The paper is framed as a feasibility study, but the physical interpretation rests on the premise that the UV excess is intrinsic to single FGK stars, a premise the authors themselves repeatedly state cannot be verified with the present data.

Significance. If the central claim survives follow-up, the paper would identify a previously neglected UV channel in old stellar populations: apparently ordinary FGK/MSTO stars with large UV excesses. The main strengths are that the catalogue and colour classifications are transparent, the analysis is reproducible in structure (the data sources, cuts, and bootstrap scheme are described in detail), the authors explicitly provide the empirical templates and galaxy inputs in Tables A.2-A.3, and the caveats about unresolved companions, GALEX PSF blending, and the small sample size are acknowledged honestly. The significance is, however, conditional: the UV-excess fraction and the group structure are based on a 37-star sample, the three-component GMM is adopted despite BIC preferring one component, and the extragalactic result is a feasibility upper limit rather than a measurement. The paper is therefore best viewed as a candidate-identification and methodology paper whose astrophysical conclusion about single-star UV emission needs confirmation.

major comments (3)
  1. [§2.3] The three-component GMM is central to defining Groups 1 and 3, which provide the UV templates used in the galaxy scaling. The paper states that BIC favors a one-component description when the number of components is allowed to vary, but three components are nevertheless adopted "to provide an approximate, data-driven counterpart to the three UV regimes". This means the group boundaries and the resulting Group 1 template set are not statistically supported by the data. Please justify the three-component choice with a model-selection argument (e.g., likelihood-ratio, cross-validated comparison, or posterior predictive check) or present a sensitivity analysis showing that the classification and the scaled galaxy fractions survive with two components or a non-parametric clustering. The dependence of the main results on this choice should be explicit.
  2. [§4, Table A.3] The headline 10-15% FGK fraction is driven by the bluest template (M_FUV = 7.79, L_FUV ~ 3.3e17 erg/s/Hz), which is roughly 45x more FUV-luminous than the Group 1 median. Its FUV-NUV ~ -0.55 is difficult to reconcile with a 4800-5000 K photosphere. The authors correctly concede in §4 that white-dwarf+FGK binaries and GALEX PSF blending cannot be excluded, and that the Gaia astrometric cuts are insensitive to WD companions. Because the astrophysical claim requires the excess to be intrinsic to single FGK stars, the most plausible alternative explanation is not excluded. Please either provide follow-up diagnostics for Group 1 (multi-epoch radial velocities, high-angular-resolution imaging, or UV spectroscopy) or re-frame the 10-15% statement as a formal upper limit contingent on the bluest object being a single star, with the scaling repeated excluding that template.
  3. [§2.2, §3.2] The H-alpha diagnostics cover 24/37 stars but only one Group 1 star. The conclusions that "strong H-alpha activity is not common" among the UV-abnormal stars and that "the present data cannot establish whether magnetic or chromospheric activity contributes" are thus not tested for the group with the largest UV excess. This limitation is acknowledged, but it leaves the activity-vs-companion distinction untested for the objects that drive the galaxy scaling. A representative H-alpha or Ca II HK sample for Group 1 is needed before any statement about the physical origin of the UV excess in the extreme population can be made.
minor comments (5)
  1. [Fig. 1-4] In the version provided, the figure captions and panel labels contain many '/uni000...' token strings. If these appear in the compiled manuscript, the figures are unreadable; please ensure the final PDF renders all axis labels and annotations correctly.
  2. [Fig. 6] The figure caption uses 'Active FGK fraction' whereas the text and Eq. (6) use f_uvx. Please unify the terminology and define 'active' explicitly as the UV-excess FGK fraction.
  3. [Table A.3] The column headers list L_nu units as erg/s/Hz, and the caption calls these 'monochromatic AB luminosities'. Please clarify in the caption the exact relation to M_AB, and note that the red and blue templates are individual stars while the median template is not a single object.
  4. [§2.4.1] The symbols M_present_star and M_formed_star are defined in Eq. (2) but not explicitly used in Table A.2. Please add the definitions or the table footnotes so that the reader can connect the table entries to the equation.
  5. [§2.4.2] Eq. (4) approximates the main-sequence lifetime by a single power law with eta=2.5. The bootstrap samples eta over a range, but the text should state clearly that the adopted M_FGK,lo=0.5 and M_FGK,hi=1.1 Msun window is a simplifying assumption and that the result depends on the assumed UV-excess fraction within this window.

Circularity Check

1 steps flagged · score 2.0 of 10

Mild structural circularity: the extragalactic 10–15% figure is a conditional mapping from an assumed UV-bright fraction and an extreme template, not an independent prediction; stellar classification itself is not circular.

  1. fitted input called prediction [Sect. 2.4.4, Eq. (10) and Sect. 3.4]
    "The resulting quantity is a physically transparent feasibility estimate: it measures the fraction of the observed UV output that would be matched by UV-bright FGK-like systems under the adopted assumptions. ... ffgk,uv(τ,f uvx)≡ LFGK_UV(τ,f uvx)/Lobs_UV ... The reddest template contributes negligibly ... while the bluest template changes the scaling sharply: under this most UV-luminous empirical case, a UV-excess FGK fraction of only ∼10–15% becomes capable of contributing substantially to the UV budget, provided that such stars are common and long-lived."

    The headline '10–15%' is not a prediction from independent physics: Eq. (10) divides an assumed FGK component luminosity (linearly proportional to the chosen template luminosity L* and to the assumed f_uvx) by the observed galaxy luminosity. For the bluest template (M_FUV=7.79), the required f_uvx that reaches unity is algebraically fixed by the assumed template luminosity and N_surv; the paper explicitly labels the exercise a 'feasibility estimate' rather than a fitted decomposition. Thus the '10–15%' restates the assumed template plus assumed fraction, not a derived constraint on the real duty cycle.

full rationale

The paper's stellar-side claims are self-contained and non-circular: the UV-abnormal classification uses the external Smith et al. (2014) locus and measured GALEX colours, the GMM is applied to observed colours, and the Halpha, metallicity and age trends are descriptive comparisons against externally fitted parameters (Unidam/PARSEC, Gaia-ESO). No step defines the UV-excess classification in terms of the galaxy result or vice versa. The only genuinely circular feature is the extragalactic scaling in Sect. 2.4–3.4, which the paper itself frames as a conditional 'feasibility estimate': Eq. (6) multiplies an assumed f_uvx by an empirical template luminosity, and Eq. (10) divides by the observed L_obs; the 'required 10–15%' for the bluest template is therefore a rearrangement of the assumed input luminosity and fraction, not an independent prediction of the FGK duty cycle. This is a mild structural circularity (the claimed headline number is partly built in), but the paper is transparent about it, and the stellar classification and template construction are independent of the galaxy data. Unresolved WD+FGK companions or GALEX blending are acknowledged by the authors as alternatives, and the catalogue is explicitly presented as a candidate list; that is a correctness/robustness concern, not a circularity of the derivation. Because the central stellar characterization is not fitted to the galaxies and the galaxy result is honestly labelled as an assumption-driven scaling, the appropriate score is low (2).

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

The central claim rests on external benchmarks (Smith UV-normal locus, Gaia-ESO parameters, GALEX photometry), standard IMF and MS-lifetime inputs, a paper-specific FGK mass window, and an assumed UV-bright fraction. No new physical entity is introduced. The most paper-specific choices are the three-component GMM and the 0.5–1.1 Msun window; these are honest modelling assumptions but they shape the results, especially the extragalactic feasibility curves.

free parameters (6)
  • Number of GMM components = 3 (BIC prefers 1)
    Sec. 2.3: the three-component GMM is adopted explicitly to mirror the three UV regimes used in galaxy studies, even though the Bayesian Information Criterion favours one component.
  • FGK mass window bounds = 0.5–1.1 Msun
    Sec. 2.4.2: M_FGK,lo=0.5 and M_FGK,hi=1.1 are chosen to represent the strongest UV-excess T-GEX templates; the authors acknowledge the choice is a simplifying assumption.
  • Main-sequence lifetime exponent eta = 2.5 (sampled N(2.5, 0.3))
    Eq. 4: textbook range is 2.5–3.5; the fiducial value 2.5 is treated as a nuisance parameter in the bootstrap.
  • Returned-mass fraction R = 0.4 (sampled N(0.4, 0.05))
    Eq. 2: adopted from Vincenzo et al. (2016); the authors state varying R over 0.2–0.4 changes the formed mass by 30–40%.
  • UV-bright FGK fraction f_uvx = varied 0–1
    Eq. 6: the central extragalactic output is a function of the assumed fraction, not a measured quantity; the 10–15% headline is the assumed fraction needed for the bluest template.
  • Representative population age tau = 7.0–7.4 Gyr for the three galaxies; 5–13 Gyr uniform when unavailable
    Table A.2 and Sec. 2.4.5: the adopted ages set the surviving-FGK count via Eq. 5 and are perturbed in the bootstrap; they are inputs, not derived here.
assumptions (7)
  • domain assumption Kroupa IMF with mass limits 0.08–100 Msun holds universally and is appropriate for quiescent galaxies.
    Sec. 2.4: the authors assume a universal Kroupa-like IMF and state that Chabrier/Salpeter give negligible differences in the FGK mass range.
  • domain assumption Main-sequence lifetime follows the power law t_MS(M) = 10 Gyr (M/Msun)^(-eta).
    Eq. 4: standard nuclear-timescale scaling; eta is allowed to vary but the functional form is assumed.
  • ad hoc to paper The restricted FGK mass window 0.5–1.1 Msun captures the MSTO population relevant to the UV excess.
    Sec. 2.4.2: the window is chosen to match the strongest UV-excess T-GEX stars; the authors admit they cannot determine whether comparable behaviour occurs among early-F stars.
  • domain assumption The Smith et al. (2014) empirical UV-normal locus is a valid external benchmark for classifying MW field FGK stars.
    Sec. 3.1: the 2/3 UV-abnormal fraction is defined relative to this digitized locus; systematic offsets in the reference would change the central number.
  • domain assumption Gaia-ESO derived Teff, logg, [Fe/H], and [Mg/Fe] are accurate, and the Gaia astrometric cuts remove most unresolved multiples.
    Sec. 2.1: the authors explicitly note that unresolved companions cannot be excluded, so this assumption is only partially satisfied.
  • domain assumption GALEX photometry is not significantly contaminated by unresolved UV-bright neighbours within the PSF.
    Sec. 4: chance alignments and compact companions within the ~4–5 arcsec GALEX PSF cannot be excluded for every source.
  • domain assumption Quiescent galaxies can be approximated by a single representative old population age for estimating the surviving FGK number.
    Sec. 2.4.2: the authors state this is not a full reconstruction of the galaxy star-formation history.

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Cite this review

Pith. "Pith review of The T-GEX project. I. Stealth UV-bright Sun-like stars in the Galaxy as candidate contributors to the UV upturn." pith.science (2026). https://pith.science/paper/N7E3PGM4

@misc{pith2026260727324,
  author       = {Pith},
  title        = {Pith review of: The T-GEX project. I. Stealth UV-bright Sun-like stars in the Galaxy as candidate contributors to the UV upturn},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N7E3PGM4}},
  note         = {Machine review of arXiv:2607.27324}
}
abstract

We constructed the Tiny Gaia-ESO + GALEX (T-GEX) catalogue by combining Gaia-ESO spectroscopy, Gaia DR3 astrometry, GALEX UV photometry, and 2MASS and AllWISE infrared measurements for 37 stars. Astrometric and spectroscopic quality cuts minimise obvious multiplicity and spectral peculiarity, although unresolved companions cannot be excluded. We classified the stars relative to the empirical FGK UV-normal locus, applied a 3-component Gaussian mixture model in the FUV-NUV versus NUV-$G$ plane, measured H$\alpha$ $\lambda 6563$ diagnostics for 24 stars, and performed an IMF-based empirical scaling calculation for three UV-upturn galaxies. Approximately 2/3 of the sample are UV-abnormal. The GMM identifies a cool, strongly UV-excess group (G1), a hotter UV-normal group (G2), and an intermediate UV-excess group (G3). G1 occupies a narrow, predominantly sub-solar [Fe/H] range and is $\alpha$-enhanced, with the highest median [Mg/Fe]. G3 has the highest median [Fe/H], while G2 spans the broadest metallicity range and has the youngest median age. G1 and G3 extend to old ages, although their youngest estimates are affected by isochrone degeneracy. Enhanced H$\alpha$ core emission occurs only among hotter G2 stars in the available subsample, but only one G1 star has H$\alpha$ coverage. The extragalactic contribution is strongly template-dependent: the median G1 template accounts for at most $\sim20\%$ of the observed UV output, whereas the bluest template could match most or all of the NUV and FUV emission if shared by $\sim10$--$15\%$ of surviving FGK stars.

Figures

Figures reproduced from arXiv: 2607.27324 by the authors.

Figure 1
Figure 1. UV-colour versus Teff relation and Kiel diagram. Left panel: Teff versus FUV–NUV colour for our stellar sample. Black diamonds denote the UV-normal stars from Smith et al. (2014), together with their defined UV-normality trend (solid line) and threshold (dotted line). The numerical data from Smith et al. (2014) were retrieved from the published figures using WebPlotDig￾itizer (Rohatgi 2024). Our sample is classified… view at source ↗
Figure 2
Figure 2. Panels (left to right): Colour–colour diagrams combining GALEX (FUV, NUV) and [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Panels (top-left to bottom-right): Gaussian kernel density distributions of key stellar parameters: [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Hα line cores for the 24 T-GEX stars with suitable spectral coverage. The line centre is marked by the vertical dashed line. The most frequent core flux level (fcore = 0.18) is shown by the horizontal solid black line, with the corresponding ±3σ interval (σ = 0.02) ind…
Figure 5
Figure 5. Figure 5: A chord diagram (top) summarising the strongest corre [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Fraction of the UV emission in quiescent galaxies that can be accounted for by FGK stars with UV excess, estimated with the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

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Works this paper leans on

86 extracted references · 1 linked inside Pith

  1. [1]

    Bahcall, J. N. & Piran, T. 1983, ApJ, 267, L77

  2. [2]

    K., Liske, J., Brown, M

    Baldry, I. K., Liske, J., Brown, M. J. I., et al. 2018, MNRAS, 474, 3875

  3. [3]

    L., Donahue, R

    Baliunas, S. L., Donahue, R. A., Soon, W. H., et al. 1995, ApJ, 438, 269 Beltrán, D. & Dantas, M. L. L. 2025, Research Notes of the American Astro- nomical Society, 9, 216 Beltrán, D., Dantas, M. L. L., Smiljanic, R., & Tissera, P. B. 2026, A&A, in preparation

  4. [4]

    & Arnouts, S

    Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393

  5. [5]

    Bertola, F., Capaccioli, M., & Oke, J. B. 1982, ApJ, 254, 494

  6. [6]

    2014, Astrophysics and Space Science, 354, 103

    Bianchi, L. 2014, Astrophysics and Space Science, 354, 103

  7. [7]

    & Gerhard, O

    Bland-Hawthorn, J. & Gerhard, O. 2016, ARA&A, 54, 529

  8. [8]

    2012, MNRAS, 427, 127

    Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127

Show all 86 references
  1. [9]

    M., Bowers, C

    Brown, T. M., Bowers, C. W., Kimble, R. A., Sweigart, A. V ., & Ferguson, H. C. 2000, ApJ, 532, 308

  2. [10]

    M., Faber, S

    Burstein, D., Bertola, F., Buson, L. M., Faber, S. M., & Lauer, T. R. 1988, ApJ, 328, 440

  3. [11]

    M., Bertone, E., Buzzoni, A., & Carraro, G

    Buson, L. M., Bertone, E., Buzzoni, A., & Carraro, G. 2006, Baltic Astronomy, 15, 49

  4. [12]

    2012, ApJ, 749, 35

    Buzzoni, A., Bertone, E., Carraro, G., & Buson, L. 2012, ApJ, 749, 35

  5. [13]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

  6. [14]

    Code, A. D. & Welch, G. A. 1979, ApJ, 228, 95

  7. [15]

    2013, ARA&A, 51, 393

    Conroy, C. 2013, ARA&A, 51, 393

  8. [16]

    2020, A&A, 640, A42

    Cretignier, M., Francfort, J., Dumusque, X., Allart, R., & Pepe, F. 2020, A&A, 640, A42

  9. [17]

    M., Wright, E

    Cutri, R. M., Wright, E. L., Conrow, T., et al. 2014, VizieR Online Data Catalog, II/328

  10. [18]

    Dantas, M. L. L., Coelho, P. R. T., de Souza, R. S., & Gonçalves, T. S. 2020, MNRAS, 492, 2996

  11. [19]

    Dantas, M. L. L., Coelho, P. R. T., & Sánchez-Blázquez, P. 2021, MNRAS, 500, 1870

  12. [20]

    Dantas, M. L. L., Smiljanic, R., Boesso, R., et al. 2023, A&A, 669, A96

  13. [21]

    Dantas, M. L. L., Smiljanic, R., de Souza, R. S., Tissera, P. B., & Magrini, L. 2025, A&A, 696, A205 de Laverny, P., Ligi, R., Crida, A., Recio-Blanco, A., & Palicio, P. A. 2025, A&A, 699, A100 de Souza, R. S. & Ciardi, B. 2015, Astronomy and Computing, 12, 100 de Souza, R. S....

  14. [22]

    S., Knigge, C., Thomson, G

    Dieball, A., Long, K. S., Knigge, C., Thomson, G. S., & Zurek, D. R. 2010, ApJ, 710, 332

  15. [23]

    T., & O’Connell, R

    Dorman, B., Rood, R. T., & O’Connell, R. W. 1993, ApJ, 419, 596

  16. [24]

    P., Norberg, P., Baldry, I

    Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12

  17. [25]

    J., Stanway, E

    Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058

  18. [26]

    2021, A&A, 649, A5

    Fabricius, C., Luri, X., Arenou, F., et al. 2021, A&A, 649, A5

  19. [27]

    2011, AJ, 142, 23

    Findeisen, K., Hillenbrand, L., & Soderblom, D. 2011, AJ, 142, 23

  20. [28]

    & Bland-Hawthorn, J

    Freeman, K. & Bland-Hawthorn, J. 2002, ARA&A, 40, 487 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023, A&A, 674, A1 García-Delgado, J. J., Dantas, M. L. L., Rebollido, I., & Smiljanic, R. 2026, arXiv e-prints, arXiv:2607.07787

  21. [29]

    C., Hourihane, A., et al

    Gilmore, G., Randich, S., Worley, C. C., Hourihane, A., et al. 2022, A&A, 666, A120 Gonçalves, G., Coelho, P., Schiavon, R., & Usher, C. 2020, MNRAS, 499, 2327

  22. [30]

    2018, ApJ, 857, 16

    Goudfrooij, P. 2018, ApJ, 857, 16

  23. [31]

    2018, The Journal of Open Source Software, 3, 695

    Green, G. 2018, The Journal of Open Source Software, 3, 695

  24. [32]

    M., Schlafly, E

    Green, G. M., Schlafly, E. F., Finkbeiner, D., et al. 2018, MNRAS, 478, 651

  25. [33]

    2014, Bioinformatics, 30, 2811–2812

    Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. 2014, Bioinformatics, 30, 2811–2812

  26. [34]

    Han, Z., Podsiadlowski, P., & Lynas-Gray, A. E. 2007, MNRAS, 380, 1098

  27. [35]

    J., Kawaler, S

    Hansen, C. J., Kawaler, S. D., & Trimble, V . 2004, Stellar Interiors: Physical

  28. [36]

    1978, A&A, 63, 383

    Hardorp, J. 1978, A&A, 63, 383

  29. [37]

    1982, A&A, 105, 120

    Hardorp, J. 1982, A&A, 105, 120

  30. [38]

    2001, The Elements of Statistical

    Hastie, T., Tibshirani, R., & Friedman, J. 2001, The Elements of Statistical

  31. [39]

    2020, ARA&A, 58, 205

    Helmi, A. 2020, ARA&A, 58, 205

  32. [40]

    Herczeg, G. J. & Hillenbrand, L. A. 2008, ApJ, 681, 594 Hernández-Pérez, F. & Bruzual, G. 2014, MNRAS, 444, 2571 Hernández-Pérez, F. & Bruzual, G. 2013, MNRAS, 431, 2612

  33. [41]

    2021, A&A, 646, L6

    Hui-Bon-Hoa, A. 2021, A&A, 646, L6

  34. [42]

    R., Pols, O

    Hurley, J. R., Pols, O. R., & Tout, C. A. 2000, MNRAS, 315, 543

  35. [43]

    Iglesias, C. A. & Rogers, F. J. 1996, ApJ, 464, 943

  36. [44]

    2022, A&A, 657, A7

    Kervella, P., Arenou, F., & Thévenin, F. 2022, A&A, 657, A7

  37. [45]

    2001, MNRAS, 322, 231

    Kroupa, P. 2001, MNRAS, 322, 231

  38. [46]

    D., van Driel, W., Le Tiran, L., Di Matteo, P., & Haywood, M

    Lehnert, M. D., van Driel, W., Le Tiran, L., Di Matteo, P., & Haywood, M. 2015, A&A, 577, A112

  39. [47]

    Leitner, S. N. & Kravtsov, A. V . 2011, ApJ, 734, 48

  40. [48]

    L., Worden, S

    Linsky, J. L., Worden, S. P., McClintock, W., & Robertson, R. M. 1979, ApJS, 41, 47

  41. [49]

    2005, MNRAS, 362, 799

    Maraston, C. 2005, MNRAS, 362, 799

  42. [50]

    C., Fanson, J., Schiminovich, D., et al

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJ, 619, L1

  43. [51]

    McLachlan, G. J. & Peel, D. 2000, Finite mixture models, Wiley series in prob- ability and statistics (New York: J. Wiley & Sons)

  44. [52]

    & Hekker, S

    Mints, A. & Hekker, S. 2017, A&A, 604, A108

  45. [53]

    & Hekker, S

    Mints, A. & Hekker, S. 2018, A&A, 618, A54

  46. [54]

    Murphy, K. P. 2013, Machine learning : a probabilistic perspective (Cambridge, Mass. [u.a.]: MIT Press)

  47. [55]

    K., Ganguly, A., & Chatterjee, S

    Nayak, P. K., Ganguly, A., & Chatterjee, S. 2024, MNRAS, 527, 6100 O’Connell, R. W. 1999, ARA&A, 37, 603

  48. [56]

    Offner, S. S. R., Moe, M., Kratter, K. M., et al. 2023, in Astronomical Society of the Pacific Conference Series, V ol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y . Aikawa, T. Muto, K. Tomida, & M. Tamura, 275

  49. [57]

    G., Rebassa-Mansergas, A., Schreiber, M

    Parsons, S. G., Rebassa-Mansergas, A., Schreiber, M. R., et al. 2016, MNRAS, 463, 2125

  50. [58]

    B., Zepf, S

    Peacock, M. B., Zepf, S. E., Maccarone, T. J., et al. 2018, MNRAS, 481, 3313

  51. [59]

    & Turck-Chièze, S

    Piau, L. & Turck-Chièze, S. 2002, ApJ, 566, 419

  52. [60]

    2024, AJ, 167, 217

    Psaradaki, I., Corrales, L., Werk, J., et al. 2024, AJ, 167, 217

  53. [61]

    Queiroz, A. B. A., Anders, F., Chiappini, C., et al. 2023, A&A, 673, A155

  54. [62]

    Queiroz, A. B. A., Anders, F., Santiago, B. X., et al. 2018, MNRAS, 476, 2556

  55. [63]

    2022, A&A, 666, A121 Article number, page 11 A&A proofs:manuscript no

    Randich, S., Gilmore, G., Magrini, L., et al. 2022, A&A, 666, A121 Article number, page 11 A&A proofs:manuscript no. paper

  56. [64]

    R., Dalessandro, E., et al

    Raso, S., Ferraro, F. R., Dalessandro, E., et al. 2017, ApJ, 839, 64

  57. [65]

    2025, A&A, 702, A185

    Reggiani, E., Cadelano, M., Lanzoni, B., et al. 2025, A&A, 702, A185

  58. [66]

    & Bovy, J

    Rix, H.-W. & Bovy, J. 2013, A&A Rev., 21, 61

  59. [67]

    2024, WebPlotDigitizer

    Rohatgi, A. 2024, WebPlotDigitizer

  60. [68]

    C., Girardi, L., et al

    Rosenfield, P., Johnson, L. C., Girardi, L., et al. 2012, ApJ, 755, 131

  61. [69]

    Salpeter, E. E. 1955, ApJ, 121, 161 Salvador-Rusiñol, N., Vazdekis, A., La Barbera, F., et al. 2020, Nature Astron- omy, 4, 252

  62. [70]

    C., Sousa, S

    Santos, N. C., Sousa, S. G., Mortier, A., et al. 2013, A&A, 556, A150

  63. [71]

    1978, The Annals of Statistics, 6

    Schwarz, G. 1978, The Annals of Statistics, 6

  64. [72]

    Shkolnik, E. L. & Barman, T. S. 2014, AJ, 148, 64

  65. [73]

    L., Liu, M

    Shkolnik, E. L., Liu, M. C., Reid, I. N., Dupuy, T., & Weinberger, A. J. 2011, ApJ, 727, 6

  66. [74]

    Sindhu, N., Subramaniam, A., & Radha, C. A. 2018, MNRAS, 481, 226

  67. [75]

    F., Cutri, R

    Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163

  68. [76]

    Smith, G. H. & Redenbaugh, A. K. 2010, PASP, 122, 1303

  69. [77]

    A., Bianchi, L., & Shiao, B

    Smith, M. A., Bianchi, L., & Shiao, B. 2014, AJ, 147, 159 Souza dos Santos, P. V ., Porto de Mello, G. F., Costa-Bhering, E., et al. 2024, MNRAS, 532, 563

  70. [78]

    1904, The American Journal of Psychology, 15, 72 Stonkut˙e, E., Koposov, S

    Spearman, C. 1904, The American Journal of Psychology, 15, 72 Stonkut˙e, E., Koposov, S. E., Howes, L. M., et al. 2016, MNRAS, 460, 1131

  71. [79]

    2016, MNRAS, 455, 4183

    Vincenzo, F., Matteucci, F., Belfiore, F., & Maiolino, R. 2016, MNRAS, 455, 4183

  72. [80]

    Wade, R. A. & Hubeny, I. 1998, ApJ, 509, 350

  73. [81]

    2011, Ap&SS, 331, 1

    Walcher, J., Groves, B., Budavári, T., & Dale, D. 2011, Ap&SS, 331, 1

  74. [82]

    W., & Alexander, D

    Weiss, A., Salaris, M., Ferguson, J. W., & Alexander, D. R. 2006, arXiv e-prints, astro

  75. [83]

    V ., et al

    Werle, A., Cid Fernandes, R., Asari, N. V ., et al. 2019, MNRAS, 483, 2382

  76. [84]

    Xin, Y ., Deng, L., & Han, Z. W. 2007, ApJ, 660, 319

  77. [85]

    K., Lee, J., Sheen, Y .-K., et al

    Yi, S. K., Lee, J., Sheen, Y .-K., et al. 2011, ApJS, 195, 22

  78. [86]

    G., Adelman, J., Anderson, John E., J., et al

    York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579 Article number, page 12 M. L. L. Dantas et al.: TheT-GEXproject Appendix A: Supplementary quantities for the stellar and extragalactic analyses This appendix provides the numerical quantities supportin...

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