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REVIEW 5 major objections 5 minor 95 references

Mind the Gap II: the near-UV fluxes of M dwarfs

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Pith's one-line read M dwarfs split into two distinct near-ultraviolet populations on the $M_{\rm NUV}$–$M_G$ diagram, with the lower branch's excess flux dominated by Fe II line forests.

desk verdict A credible two-branch NUV split for M dwarfs, with the Fe II mechanism overreaching; binarity is a real, untested confounder. read the letter →

arxiv 2501.04806 v2 pith:VKUUQ2UD submitted 2025-01-08 astro-ph.SR

classification astro-ph.SR
keywords Mdwarfstarsnear-ultravioletphotometryGALEXGaiamainsequenceFeIIlineforeststellarchromospheresultraviolet
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that M dwarfs do not follow a single smooth relation in the near-ultraviolet: on the $M_{\rm NUV}$ versus $M_G$ diagram they separate into two distinct branches, an upper branch with low NUV flux and a lower branch with 3 to 25 times more NUV flux at a given mass. The split begins near spectral type M2 ($M_G \sim 9.4$), close to the main-sequence gap and to the onset of molecular hydrogen formation that changes energy transport in the atmosphere. Using archival Hubble spectra, the authors show that the excess flux of the lower branch is dominated by Fe II line forests near 2400 and 2600 Å, not by the Mg II doublet at 2800 Å. If correct, this means photometric relations calibrated in the optical and near-infrared cannot be extrapolated into the NUV, and that M dwarfs of identical mass can present very different ultraviolet environments to their planets.

What carries the argument

The key machinery is the $M_{\rm NUV}$ vs. $M_G$ diagram, where Gaia absolute $G$ magnitude serves as a mass proxy, combined with the GALEX NUV bandpass. Two-dimensional Gaussian mixture clustering assigns stars to upper and lower branch fits, and paired HST/STIS spectra convolved with the GALEX NUV filter curve identify which emission lines carry the flux difference. The paper also uses the known kink in the mass-luminosity relation at M2, attributed to H2 formation and improved convective energy transport, as the proposed physical trigger for the NUV anomaly.

What would settle it

Take new HST/STIS spectra of a lower-branch early-M dwarf (M0–M2) outside the Hyades that has the same $M_G$ as an upper-branch star, and check whether Fe II forests still dominate the flux difference; if the excess instead comes from Mg II, continuum, or other lines, the Fe II mechanism fails. A second test: if a deeper NUV survey such as UVEX fills the gap between the two branches for late M dwarfs, the two-population claim would be an artifact of GALEX sensitivity limits rather than a physical split.

Watch

Extended reading notes

Core claim

The central discovery is that the near-ultraviolet main sequence of M dwarfs is not a single sequence. In a sample of 10,234 GALEX/Gaia stars, the authors find two well-separated populations on the $M_{\rm NUV}$ vs. $M_G$ diagram: an upper branch whose NUV fluxes match PARSEC photospheric model isochrones, and a lower branch whose members are brighter in NUV by factors of 3 to 25, with the ratio increasing for later, lower-mass M dwarfs. The number of stars on the lower branch rises sharply near M2 ($M_G \sim 9.4$), coincident with the main-sequence gap and with atmospheric H2 formation. Archival HST/STIS spectra of two matched pairs show that the lower-branch stars have additional emission from Fe II line forests near 2400 and 2600 Å, which after convolution with the GALEX NUV bandpass account for most of the accumulated excess flux; the Mg II doublet contributes less than 20%. The authors further show that most young moving-group members and fast rotators fall on the lower branch, though many lower-branch stars are neither young nor fast-rotating, and they find evidence that NUV flaring stars may form a third, even brighter population.

Load-bearing premise

The claim that Fe II line forests cause the excess NUV flux rests on just two pairs of HST spectra, and the lower-branch stars with full NUV spectra are almost all mid-M dwarfs and Hyades members because of HST bright-object restrictions; if those few stars are not representative of the entire lower branch, the mechanism does not generalize.

Editorial extensions

If this is right

  • A single polynomial mass-luminosity or color-magnitude relation cannot describe M dwarfs in the NUV; models and surveys that treat NUV flux as a smooth function of mass will mis-estimate UV output for a large fraction of stars.
  • Because the same-mass M dwarf can differ by factors of 3 to 25 in NUV flux, the ultraviolet radiation environment, and therefore photochemistry and potential surface habitability, of exoplanets around M dwarfs depends on which branch the host star occupies.
  • The excess flux grows toward later types, so lower-mass M dwarfs on the lower branch are relatively the most NUV-bright, which must be accounted for when interpreting GALEX-based activity surveys.
  • Mg II emission is present in essentially all M dwarfs, yet it is not the driver of the branch separation; future UV studies should target Fe II-rich wavelength regions rather than only the traditional Mg II diagnostic.

Reading between the lines

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

  • If the Fe II dominance holds across all masses, then NUV band definitions matter: a filter centered near 2400–2800 Å strongly selects for the lower-branch population, and comparisons between GALEX and Swift NUV measurements could systematically differ because of their different bandpasses.
  • The proposed link between H2 formation at M2 and enhanced NUV emission is suggestive but untested; a model that couples H2-driven convection with chromospheric heating could predict where the lower branch should appear as a function of metallicity, offering a testable extension.
  • The existence of lower-branch stars that are neither young nor fast-rotating, plus the paper's third, flaring population, suggests unresolved binarity or non-linear wave heating may be important; high-contrast imaging of such outliers would separate these alternatives.
  • If the same two-branch structure appears for K dwarfs, as the paper's cursory FUV check hints, then the phenomenon is not specific to fully convective M dwarfs and the M2/H2 coincidence may be accidental; the authors leave this as future work.
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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

5 major / 5 minor

Summary. The paper analyzes a GALEX/Gaia cross-matched sample of 10,234 nearby M dwarfs and claims that M dwarfs form two distinct populations in the near-ultraviolet, visible as upper and lower branches on the M_NUV versus M_G diagram. The authors report that the split begins near spectral type M2/M_G~9.4, that the flux gap between branches grows from roughly a factor of 3 to a factor of 25 toward lower masses, and that archival HST/STIS spectra of two branch-matched pairs show the excess is dominated by Fe II line forests near 2400 and 2800 Angstroms rather than by the Mg II doublet. They further show that H-alpha-active stars, fast rotators, and young moving-group candidates preferentially fall on the lower branch, while not all lower-branch stars are young or fast rotators. The paper concludes that standard smooth polynomial main-sequence relations do not hold in the NUV band.

Significance. If the two-branch interpretation is correct, the result is significant for M dwarf characterization and exoplanet UV-environment studies, because it implies that single M dwarfs of the same mass can differ by factors of 3 to 25 in NUV output and that smooth relations calibrated on optical and near-IR bands cannot be extrapolated to GALEX NUV. The paper has genuine strengths: it uses a large, well-defined all-sky sample; it anchors the branch interpretation with independent external samples (H-alpha surveys, rotation periods, BANYAN young-star candidates); it compares the upper branch to a PARSEC photosphere and finds consistency; and it explicitly states the limitations of its spectroscopic sample in Section 11. The central photometric bimodality is visible in the data, and the H-alpha and rotation comparisons provide independent supporting evidence. The main weaknesses are the unquantified role of unresolved binaries and the very limited spectroscopic basis for the Fe II mechanism.

major comments (5)
  1. [Section 2 and Section 4] The sample selection does not exclude equal-mass or close unresolved binaries: RUWE<1.4 and the 15-arcsecond neighbor cut with dGRP<4.0 remove only wide, unequal-flux companions, while Section 4 explicitly lists 'equal mass short-period binaries' as a candidate explanation for lower-branch stars and Section 11 says unresolved binaries 'could be the main reason' for relatively inactive lower-branch stars. Because unresolved binaries elevate NUV flux without any chromospheric Fe II enhancement, the paper needs to quantify the binary fraction on each branch or test whether lower-branch excess correlates with radial-velocity variability, astrometric signatures, or high-resolution imaging before concluding that the two populations are a stellar activity dichotomy rather than partly a binarity effect.
  2. [Section 10 and Section 11] The claim that Fe II line forests dominate the excess NUV flux rests on only two spectral pairs, GJ163/2MA0417+1454 and GJ699/2MA0358+1237, and the authors acknowledge in Section 11 that lower-branch stars with full NUV coverage are almost exclusively mid-M dwarfs and Hyades members because of HST bright-object restrictions. This selection bias limits both the mass range and the age/activity range over which the Fe II mechanism is established, so the statement that Fe II forests dominate the excess 'at a given mass' is not yet demonstrated across the full M dwarf branch. The authors should either present additional lower-branch spectra (including early M dwarfs and non-Hyades stars) or explicitly restrict the mechanistic conclusion to the mid-M/Hyades regime where data exist.
  3. [Section 7 and Table 2] The fitted branch slopes, intercepts, and the resulting excess ratios 3x, 13x, and 25x in Figure 7 and the 7x/36x FUV values are presented without uncertainties. Table 2 gives coefficients without errors, the 0.4-magnitude branch classification cutoff in Section 7.1 is set by inspection, and the FUV upper-branch line is fitted by eye. Since the excess ratios are derived from these fitted lines via Equation (5), the paper should provide bootstrap or covariance-based uncertainties on the branch parameters, check the sensitivity of the derived excess ratios to the classification offset, and report error bars on the magnitude differences and flux ratios. Without this, the central quantitative claims cannot be evaluated for significance.
  4. [Section 7.2 and Figure 7] The FUV analysis is considerably less rigorous than the NUV analysis: the upper branch is fitted by eye, the lower branch is fitted after excluding the by-eye upper branch, and the assumption of two populations in FUV is justified only by a cursory examination of K dwarfs. Given that the FUV sample is sparse and that the claimed FUV excess ratios are even larger than the NUV ones, the FUV two-branch decomposition and excess ratios should be presented as provisional or supported by a reproducible fitting procedure with uncertainties, rather than as results of comparable standing to the NUV fits.
  5. [Section 3 and Section 11] The abstract and Section 3 present the H2-formation/M2-anomaly connection as a suggested mechanism, but Section 11 correctly states that the connection between H2 formation and excess NUV flux 'is yet to be studied.' This is a reasonable framing, but the paper should apply the same caution to the Fe II mechanism: the spectra in Figure 10 show that Fe II lines are strong in the two Hyades lower-branch stars, but no synthetic or empirical demonstration is given that Fe II opacity alone can produce the observed photometric excess across the branch. A quantitative estimate of the Fe II contribution using the two available pairs, including the uncertainty from the nonzero Delta M_G in the second pair, would strengthen the mechanistic claim.
minor comments (5)
  1. [Abstract and Introduction] The abstract contains several typographical and grammatical issues, including 'In this study of utilizing a catalog' and the broken 'MN U V' formatting; these should be corrected in a final language pass.
  2. [Section 3] The claim that 'the improved energy transport may carry extra energy into the atmosphere' is speculative and is not directly tested; consider moving it more explicitly into the discussion of possible mechanisms rather than presenting it near the primary results.
  3. [Section 7.2, footnotes] The reference to Pedregosa et al. 2020 is cited in the text as 2020, but the bibliography lists Pedregosa et al. 2011; the citation year should be checked.
  4. [Figure 9 and Table 4] The three highlighted mid-M dwarfs have very different metallicities and rotation periods, so the statement that 'the Mg II line strength increases... the absolute NUV magnitudes continue to decrease' should explicitly note the small sample size and the potential confounding role of metallicity, as the text partially does but the figure caption does not.
  5. [Section 10] In the second spectral pair, GJ699 and 2MA0358+1237 have Delta M_G = 0.09 mag, which is larger than the first pair's Delta M_G and could imply a small mass difference; the text acknowledges this, but the comparison would be clearer if the mass difference were propagated into the cumulative excess-flux calculation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: branch fits are descriptive summaries, not predictions, and the Fe II mechanism is supported by independent spectra.

full rationale

The derivation chain is self-contained. The two-population structure is inferred directly from GALEX/Gaia photometry via a Gaussian mixture, and the branch lines in Table 2 are descriptive fits to the same data. The excess ratios in Section 7.2 are arithmetic transformations of those fitted lines, not predictions validated against held-out data, so no fitted parameter is renamed as a prediction. External anchors support the interpretation: the PARSEC/BT-Settl isochrone matches the upper branch, H-alpha absorption/emission samples from MEarth, CARMENES, and LAMOST map onto the branches, rotation samples from K2 and Newton et al. (2017) place fast rotators on the lower branch, and BANYAN young-star candidates preferentially fall on the lower branch. The Fe II dominance conclusion rests on two HST/STIS pairs, but the paper explicitly acknowledges the Hyades/mid-M selection bias in Section 11; that is a generalization risk, not a circular step. Self-citations to Jao et al. (2018, 2023) supply main-sequence gap boundaries and rotation classification conventions, but those are context and do not carry the central NUV claim. The binary-contamination concern raised in the skeptical reading is a correctness and sample-bias issue, not a circularity issue.

Assumptions & free parameters 9 free parameters · 4 assumptions · 0 invented entities

The central claim depends on fitted branch lines and a chosen classification offset, on the reliability of the public catalogs, and on the representativeness of four HST spectra. No new physical entities are introduced.

free parameters (9)
  • NUV upper branch slope a = 0.65035
    Fitted by GMM clustering to the upper branch in MNUV versus MG (Table 2); used to compute excess flux ratios.
  • NUV upper branch intercept b = -3.32947
    Fitted by GMM clustering to the upper branch in MNUV versus MG (Table 2).
  • NUV lower branch slope a = 1.15668
    Fitted by GMM clustering to the lower branch in MNUV versus MG (Table 2).
  • NUV lower branch intercept b = -10.38865
    Fitted by GMM clustering to the lower branch in MNUV versus MG (Table 2).
  • FUV upper branch slope a = 6.28459
    Fitted to the sparse FUV upper branch (Table 2).
  • FUV upper branch intercept b = -103.02273
    Fitted to the sparse FUV upper branch (Table 2).
  • FUV lower branch slope a = 0.98846
    Fitted by GMM clustering to the FUV lower branch (Table 2).
  • FUV lower branch intercept b = -11.65192
    Fitted by GMM clustering to the FUV lower branch (Table 2).
  • branch classification offset = 0.4 mag
    Chosen threshold below the upper branch line for plotting classification (Section 7.1).
assumptions (4)
  • domain assumption The GUVmatch AISxGaiaDR2 catalog cross-match is reliable for selecting M dwarfs with GALEX NUV photometry.
    Assumed throughout; the catalog is an HLSP from Bianchi & Shiao (2020).
  • domain assumption The PARSEC isochrone with BT-Settl atmospheres represents photospheric-only NUV fluxes for the upper branch.
    Used in Section 7.2 to argue upper branch stars match photospheric models.
  • ad hoc to paper The Gaussian mixture model with full covariance is an appropriate unsupervised separation of the two branches.
    Section 7.1; the paper notes there is no distinctive way to separate the branches, so the GMM defines them.
  • domain assumption The line identifications in the HST/STIS spectra are correct.
    Section 10 relies on these identifications to attribute the excess flux to Fe II.

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Cite this review

Pith. "Pith review of Mind the Gap II: the near-UV fluxes of M dwarfs." pith.science (2026). https://pith.science/paper/VKUUQ2UD

@misc{pith2026250104806,
  author       = {Pith},
  title        = {Pith review of: Mind the Gap II: the near-UV fluxes of M dwarfs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VKUUQ2UD}},
  note         = {Machine review of arXiv:2501.04806}
}
abstract

Because of the continuous variations in mass, metallicity, and opacity, dwarf stars are distributed along the main sequence on optical and near-IR color-magnitude diagrams following a smooth polynomial. In this study of utilizing a catalog of cross-matched GALEX and Gaia sources, we identify two distinct populations of M dwarfs in the near-ultraviolet (NUV) band on the $M_{NUV}$ vs. $M_G$ diagram. We also reveal a pronounced increase in the number of stars exhibiting high NUV fluxes near the spectral type M2 or $M_G\sim9.4$, coinciding with the $H_2$ formation in the atmosphere to improve the energy transportation at the surface. This suggests that certain yet-to-be-understood stellar mechanisms drive heightened activity in the NUV band around the effective temperature of M2 and later types of M dwarfs. Through examination of archival Hubble Space Telescope spectra, we show that Fe II line forests at $\sim$2400A and 2800A dominate the spectral features in the GALEX NUV bandpass, contributing to the observed excess fluxes at a given mass between the two populations. Additionally, our investigation indicates that fast rotators and young stars likely increase brightness in the NUV band, but not all stars with bright NUV fluxes are fast rotators or young stars.

Figures

Figures reproduced from arXiv: 2501.04806 by the authors.

Figure 1
Figure 1. Shared axes plots for the GAGDR3 sample. The center plot is the HRD in MG vs. BP − RP. Three thin red lines mark the general distribution of the main sequence. The top and bottom red lines mark the envelopes of the main sequence, and the center red line is the best-fitted line for the main sequence. The thick angled red line indicates the top edge of the main sequence gap. All these red lines are defined empirically… view at source ↗
Figure 2
Figure 2. Magnitude limits of GALEX at various distances. The top two plots show apparent magnitudes in NUV and FUV for our sample (black dots) against distances. Two red dashed lines mark the approximate GALEX apparent magnitude limits at 23.4 and 22.9 mags for NUV and FUV bands, respectively. The bottom four plots show the absolute magnitude limits in the NUV and FUV bands at various distances marked by shaded gray boxes, a… view at source ↗
Figure 3
Figure 3. The four-panel figure demonstrates the NUV − RP color shift identified by Cifuentes et al. (2020) at G − J ∼2.5 is not at the interior transition based on the GAGDR3 sample. The top two panels show the distributions of stars in NUV − RP vs. G − J and MG vs. MNUV plots. We also approximately separate stars into upper branch (orange dots) and lower branch (blue dots) using the line later discussed in section 7 and [P… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Distributions of stars from GAGDR3 (blue dots) and Schneider & Shkolnik (2018) on the HRD (panels a and c) and MG vs MNUV diagram (panels b and d). Single young and field stars from Schneider & Shkolnik (2018) are shown in red and black dots, respectively. Red lines ar…
Figure 5
Figure 5. Figure 5: Distribution of Hα activity from M dwarfs in the MEarth, CARMENES, and LAMOST surveys. The left plot shows their distributions on the HRD, and the right plot shows their distributions on the MG vs MNUV plot. The two populations of Hα activity mimic the two branches in …
Figure 6
Figure 6. Figure 6: Distributions of stars with rotation period measurement from the Kepler K2 Campaigns in Reinhold & Hekker (2020) and from nearby stars in Newton et al. (2017). The left plot shows their distributions on the HRD, and the right plot shows their distributions on the MG vs…
Figure 7
Figure 7. Figure 7: The GAGDR3 sample on the HRD (panels a and c), MG vs MNUV (panel b), and MG vs MF UV (panel d) diagrams. Orange dots represent stars on the upper branch, and blue dots are stars on the lower branch. The red and blue dashed lines represent the fitted distributions based…
Figure 8
Figure 8. Figure 8: Flaring M dwarfs in the NUV band on various diagrams: the HRD (panel a), MNUV vs. MG (panel b), logEf laring vs. MGG (panel c), and logE vs. distance (panel d). Red and blue dots are stars with and without detectable NUV flaring in Rekhi et al. (2023), respectively. Mo…
Figure 9
Figure 9. Figure 9: Stars with Mg II indices on HRD (panel a), MG vs. MNUV (panel b), and MG vs. Mg II index diagrams (panel c). Stars from Pal et al. (2023) are shown in black dots. An additional 38 stars retrieved from the MAST with the STIS￾MAMA+G230L observing configuration are shown …
Figure 10
Figure 10. Figure 10: Spectral comparisons between stars on different branches. The left column compares spectra for GJ163 and 2MA0417+1454, and the right column is for GJ699 and 2MA0358+1237. (Top) Spectra are calibrated to a distance of 10 pc, with the red spectra shifted upward relative…

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Works this paper leans on

95 extracted references · 5 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    pf9>쨴 T ޮR6 c-t*U Tf˧ȧ H@ &3

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

  4. [4]

    doi:10.1051/0004-6361/201527078

    Astudillo-Defru, N., Delfosse, X., Bonfils, X., et al.\ 2017, , 600, A13. doi:10.1051/0004-6361/201527078

  5. [5]

    P., Tollerud, E

    Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al.\ 2013, , 558, A33. doi:10.1051/0004-6361/201322068

  6. [6]

    M., Sip o cz, B

    Astropy Collaboration, Price-Whelan, A. M., Sip o cz, B. M., et al.\ 2018, , 156, 123. doi:10.3847/1538-3881/aabc4f

  7. [7]

    doi:10.5281/zenodo.7530476

    Basri, G.\ 2022, The 21st Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun, 114. doi:10.5281/zenodo.7530476

  8. [8]

    F., Henry, T

    Benedict, G. F., Henry, T. J., Franz, O. G., et al.\ 2016, , 152, 141. doi:10.3847/0004-6256/152/5/141

Show all 95 references
  1. [9]

    A., Huber, D., van Saders, J

    Berger, T. A., Huber, D., van Saders, J. L., et al.\ 2020, , 159, 280. doi:10.3847/1538-3881/159/6/280

  2. [10]

    doi:10.3847/1538-4365/aa7053

    Bianchi, L., Shiao, B., & Thilker, D.\ 2017, , 230, 24. doi:10.3847/1538-4365/aa7053

  3. [11]

    & Shiao, B.\ 2020, , 250, 36

    Bianchi, L. & Shiao, B.\ 2020, , 250, 36. doi:10.3847/1538-4365/aba2d7

  4. [12]

    C.\ 1976, Ph.D

    Boeshaar, P. C.\ 1976, Ph.D. Thesis

  5. [13]

    doi:10.1051/0004-6361:20053046

    Bonfils, X., Delfosse, X., Udry, S., et al.\ 2005, , 442, 635. doi:10.1051/0004-6361:20053046

  6. [14]

    S., von Braun, K., van Belle, G., et al.\ 2012, , 757, 112

    Boyajian, T. S., von Braun, K., van Belle, G., et al.\ 2012, , 757, 112. doi:10.1088/0004-637X/757/2/112

  7. [15]

    R., Vedantham, H

    Callingham, J. R., Vedantham, H. K., Shimwell, T. W., et al.\ 2021, Nature Astronomy, 5, 1233. doi:10.1038/s41550-021-01483-0

  8. [16]

    Cannon, A. J. & Pickering, E.\ 1918, Annals of the Astronomical Observatory of Harvard College, 1918-1949, Cambridge, Mass.: Astronomical Observatory of Harvard College, 1918

  9. [17]

    doi:10.1111/j.1365-2966.2008.13573.x

    Casagrande, L., Flynn, C., & Bessell, M.\ 2008, , 389, 585. doi:10.1111/j.1365-2966.2008.13573.x

  10. [18]

    L., Heintz, T

    Chiti, F., van Saders, J. L., Heintz, T. M., et al.\ 2024, arXiv:2403.12129. doi:10.48550/arXiv.2403.12129

  11. [19]

    & Baraffe, I.\ 2000, , 38, 337

    Chabrier, G. & Baraffe, I.\ 2000, , 38, 337. doi:10.1146/annurev.astro.38.1.337

  12. [20]

    doi:10.1093/mnras/stu1605

    Chen, Y., Girardi, L., Bressan, A., et al.\ 2014, , 444, 2525. doi:10.1093/mnras/stu1605

  13. [21]

    doi:10.1051/0004-6361/202347111

    Chevalier, S., Babusiaux, C., Merle, T., et al.\ 2023, , 678, A19. doi:10.1051/0004-6361/202347111

  14. [22]

    A., Cort \'e s-Contreras, M., et al.\ 2020, , 642, A115

    Cifuentes, C., Caballero, J. A., Cort \'e s-Contreras, M., et al.\ 2020, , 642, A115. doi:10.1051/0004-6361/202038295

  15. [23]

    O., & Jorgensen, H

    Copeland, H., Jensen, J. O., & Jorgensen, H. E.\ 1970, , 5, 12

  16. [24]

    Davenport, J. R. A.\ 2016, , 829, 23. doi:10.3847/0004-637X/829/1/23

  17. [25]

    Debes, J., Sankrit, R., Fischer, T., et al.\ 2024, Instrument Science Report COS 2024-01, 31 pages

  18. [26]

    B., Henry, T

    Dieterich, S. B., Henry, T. J., Jao, W.-C., et al.\ 2014, , 147, 94. doi:10.1088/0004-6256/147/5/94

  19. [27]

    A., Montes, D., et al.\ 2019, , 621, A126

    D \' ez Alonso, E., Caballero, J. A., Montes, D., et al.\ 2019, , 621, A126. doi:10.1051/0004-6361/201833316

  20. [28]

    T., Ag \"u eros, M

    Douglas, S. T., Ag \"u eros, M. A., Covey, K. R., et al.\ 2016, , 822, 47. doi:10.3847/0004-637X/822/1/47

  21. [29]

    E., Fossati, L., Koskinen, T., et al.\ 2020, , 159, 111

    Cubillos, P. E., Fossati, L., Koskinen, T., et al.\ 2020, , 159, 111. doi:10.3847/1538-3881/ab6a0b

  22. [30]

    M., Stancil, P

    Fontenla, J. M., Stancil, P. C., & Landi, E.\ 2015, , 809, 157. doi:10.1088/0004-637X/809/2/157

  23. [31]

    M., Linsky, J

    Fontenla, J. M., Linsky, J. L., Garrison, J., et al.\ 2016, , 830, 154. doi:10.3847/0004-637X/830/2/154

  24. [32]

    doi:10.1051/0004-6361/202345839

    Fouqu \'e , P., Martioli, E., Donati, J.-F., et al.\ 2023, , 672, A52. doi:10.1051/0004-6361/202345839

  25. [33]

    France, K., Loyd, R. O. P., Youngblood, A., et al.\ 2016, , 820, 89. doi:10.3847/0004-637X/820/2/89

  26. [34]

    S., Kowalski, A., France, K., et al.\ 2019, , 871, L26

    Froning, C. S., Kowalski, A., France, K., et al.\ 2019, , 871, L26. doi:10.3847/2041-8213/aaffcd

  27. [35]

    E., Malo, L., et al.\ 2018, , 856, 23

    Gagn \'e , J., Mamajek, E. E., Malo, L., et al.\ 2018, , 856, 23. doi:10.3847/1538-4357/aaae09

  28. [36]

    & Faherty, J

    Gagn \'e , J. & Faherty, J. K.\ 2018, , 862, 138. doi:10.3847/1538-4357/aaca2e

  29. [37]

    Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al.\ 2021, , 649, A1. doi:10.1051/0004-6361/202039657

  30. [38]

    N., Zhan, Z., Seager, S., et al.\ 2020, , 159, 60

    G \"u nther, M. N., Zhan, Z., Seager, S., et al.\ 2020, , 159, 60. doi:10.3847/1538-3881/ab5d3a

  31. [39]

    L., Gizis, J

    Hawley, S. L., Gizis, J. E., & Reid, I. N.\ 1996, , 112, 2799. doi:10.1086/118222

  32. [40]

    L., Davenport, J

    Hawley, S. L., Davenport, J. R. A., Kowalski, A. F., et al.\ 2014, , 797, 121. doi:10.1088/0004-637X/797/2/121

  33. [41]

    Henry, T. J. & Jao, W.-C.\ 2024, , 62, 593. doi:10.1146/annurev-astro-052722-102740

  34. [42]

    Hunter, J.D., \ 2007, Computing in Science & Engineering, 9, 90

  35. [43]

    J., Gies, D

    Jao, W.-C., Henry, T. J., Gies, D. R., et al.\ 2018, , 861, L11. doi:10.3847/2041-8213/aacdf6

  36. [44]

    J., White, R

    Jao, W.-C., Henry, T. J., White, R. J., et al.\ 2023, , 166, 63. doi:10.3847/1538-3881/ace2bb

  37. [45]

    V., Sch \"o fer, P., Lamert, A., et al.\ 2018, , 614, A76

    Jeffers, S. V., Sch \"o fer, P., Lamert, A., et al.\ 2018, , 614, A76. doi:10.1051/0004-6361/201629599

  38. [46]

    D., Henry, T

    Kirkpatrick, J. D., Henry, T. J., & McCarthy, D. W.\ 1991, , 77, 417. doi:10.1086/191611

  39. [47]

    F., Wisniewski, J

    Kowalski, A. F., Wisniewski, J. P., Hawley, S. L., et al.\ 2019, , 871, 167. doi:10.3847/1538-4357/aaf058

  40. [48]

    doi:10.1126/science.1067524

    Kroupa, P.\ 2002, Science, 295, 82. doi:10.1126/science.1067524

  41. [49]

    R., Harrison, F

    Kulkarni, S. R., Harrison, F. A., Grefenstette, B. W., et al.\ 2021, arXiv:2111.15608. doi:10.48550/arXiv.2111.15608

  42. [50]

    L.\ 2017, , 55, 159

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

  43. [51]

    L., Wood, B

    Linsky, J. L., Wood, B. E., Youngblood, A., et al.\ 2020, , 902, 3. doi:10.3847/1538-4357/abb36f

  44. [52]

    Loyd, R. O. P., France, K., Youngblood, A., et al.\ 2016, , 824, 102. doi:10.3847/0004-637X/824/2/102

  45. [53]

    Loyd, R. O. P., Shkolnik, E. L., Schneider, A. C., et al.\ 2021, , 907, 91. doi:10.3847/1538-4357/abd0f0

  46. [54]

    peng ., Zhang, L.-

    Lu, H.-. peng ., Zhang, L.-. yun ., Shi, J., et al.\ 2019, , 243, 28. doi:10.3847/1538-4365/ab2f8f

  47. [55]

    R., et al.\ 2020, , 638, A20

    Magaudda, E., Stelzer, B., Covey, K. R., et al.\ 2020, , 638, A20. doi:10.1051/0004-6361/201937408

  48. [56]

    W., Dupuy, T., Kraus, A

    Mann, A. W., Dupuy, T., Kraus, A. L., et al.\ 2019, , 871, 63. doi:10.3847/1538-4357/aaf3bc

  49. [57]

    NAtional Academies of Sciences, Engineering, and Medicine

    National Academies of Sciences, E.\ 2021, Pathways to Discovery in Astronomy and Astrophysics for the 2020s, Consenses Study Report. NAtional Academies of Sciences, Engineering, and Medicine. 2021. Washington, DC: The National Academies Press, 2021.. doi:10.17226/26141

  50. [58]

    R., Irwin, J., Charbonneau, D., et al.\ 2016, , 821, L19

    Newton, E. R., Irwin, J., Charbonneau, D., et al.\ 2016, , 821, L19. doi:10.3847/2041-8205/821/1/L19

  51. [59]

    R., Irwin, J., Charbonneau, D., et al.\ 2017, , 834, 85

    Newton, E. R., Irwin, J., Charbonneau, D., et al.\ 2017, , 834, 85. doi:10.3847/1538-4357/834/1/85

  52. [60]

    T., Costa, G., Girardi, L., et al.\ 2022, , 665, A126

    Nguyen, C. T., Costa, G., Girardi, L., et al.\ 2022, , 665, A126. doi:10.1051/0004-6361/202244166

  53. [61]

    doi:10.3847/1538-4365/accea7

    Pal, T., Khan, I., Worthey, G., et al.\ 2023, , 266, 41. doi:10.3847/1538-4365/accea7

  54. [62]

    G., G \"a nsicke, B

    Parsons, S. G., G \"a nsicke, B. T., Marsh, T. R., et al.\ 2018, , 481, 1083. doi:10.1093/mnras/sty2345

  55. [63]

    R., Barclay, T., Youngblood, A., et al.\ 2024, , 971, 24

    Paudel, R. R., Barclay, T., Youngblood, A., et al.\ 2024, , 971, 24. doi:10.3847/1538-4357/ad487d

  56. [64]

    L., et al.\ 2020, , 895, 5

    Peacock, S., Barman, T., Shkolnik, E. L., et al.\ 2020, , 895, 5. doi:10.3847/1538-4357/ab893a

  57. [65]

    \ 2011, Journal of Machine Learning Research, 12, 2825--2830,

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. \ 2011, Journal of Machine Learning Research, 12, 2825--2830,

  58. [66]

    & Hekker, S.\ 2020, , 635, A43

    Reinhold, T. & Hekker, S.\ 2020, , 635, A43. doi:10.1051/0004-6361/201936887

  59. [67]

    I., Vieytes, M

    Peralta, J. I., Vieytes, M. C., Mendez, A. M. P., et al.\ 2023, , 676, A18. doi:10.1051/0004-6361/202346156

  60. [68]

    S., Youngblood, A., & France, K.\ 2021, , 918, 40

    Pineda, J. S., Youngblood, A., & France, K.\ 2021, , 918, 40. doi:10.3847/1538-4357/ac0aea

  61. [69]

    doi:10.3847/1538-4357/ace5ac

    Rekhi, P., Ben-Ami, S., Perdelwitz, V., et al.\ 2023, , 955, 24. doi:10.3847/1538-4357/ace5ac

  62. [70]

    M.\ 2014, , 794, 144

    Reiners, A., Sch \"u ssler, M., & Passegger, V. M.\ 2014, , 794, 144. doi:10.1088/0004-637X/794/2/144

  63. [71]

    B., Xu, J., Thompson, S

    Rimmer, P. B., Xu, J., Thompson, S. J., et al.\ 2018, Science Advances, 4, eaar3302. doi:10.1126/sciadv.aar3302

  64. [72]

    doi:10.1088/0004-637X/806/1/137

    Rugheimer, S., Segura, A., Kaltenegger, L., et al.\ 2015, , 806, 137. doi:10.1088/0004-637X/806/1/137

  65. [73]

    doi:10.1088/0004-637X/809/1/57

    Rugheimer, S., Kaltenegger, L., Segura, A., et al.\ 2015, , 809, 57. doi:10.1088/0004-637X/809/1/57

  66. [74]

    doi:10.1051/0004-6361/201118179

    Saur, J., Grambusch, T., Duling, S., et al.\ 2013, , 552, A119. doi:10.1051/0004-6361/201118179

  67. [75]

    & Shibata, K.\ 2021, , 919, 29

    Sakaue, T. & Shibata, K.\ 2021, , 919, 29. doi:10.3847/1538-4357/ac0e34

  68. [76]

    Shkolnik, E. L. & Barman, T. S.\ 2014, , 148, 64. doi:10.1088/0004-6256/148/4/64

  69. [77]

    J., Hawley, S

    Schmidt, S. J., Hawley, S. L., West, A. A., et al.\ 2015, , 149, 158. doi:10.1088/0004-6256/149/5/158

  70. [78]

    Schneider, A. C. & Shkolnik, E. L.\ 2018, , 155, 122. doi:10.3847/1538-3881/aaaa24

  71. [79]

    M., Cifuentes, C., et al.\ 2019, , 625, A68

    Schweitzer, A., Passegger, V. M., Cifuentes, C., et al.\ 2019, , 625, A68. doi:10.1051/0004-6361/201834965

  72. [80]

    K., Lavvas, P., Ballester, G

    Sing, D. K., Lavvas, P., Ballester, G. E., et al.\ 2019, , 158, 91. doi:10.3847/1538-3881/ab2986

  73. [81]

    V., Reiners, A., et al.\ 2019, , 623, A44

    Sch \"o fer, P., Jeffers, S. V., Reiners, A., et al.\ 2019, , 623, A44. doi:10.1051/0004-6361/201834114

  74. [82]

    V., et al.\ 2020, , 890, 133

    Souto, D., Cunha, K., Smith, V. V., et al.\ 2020, , 890, 133. doi:10.3847/1538-4357/ab6d07

  75. [83]

    e , S., Boyle, R

    Sperauskas, J., Barta s i \= u t \. e , S., Boyle, R. P., et al.\ 2016, , 596, A116. doi:10.1051/0004-6361/201527850

  76. [84]

    G., Oelkers, R

    Stassun, K. G., Oelkers, R. J., Paegert, M., et al.\ 2019, , 158, 138. doi:10.3847/1538-3881/ab3467

  77. [85]

    C., Backus, P

    Tarter, J. C., Backus, P. R., Mancinelli, R. L., et al.\ 2007, Astrobiology, 7, 30. doi:10.1089/ast.2006.0124

  78. [86]

    B.\ 2005, Astronomical Data Analysis Software and Systems XIV, 347, 29

    Taylor, M. B.\ 2005, Astronomical Data Analysis Software and Systems XIV, 347, 29

  79. [87]

    L., et al.\ 2021, , 909, 61

    Tilipman, D., Vieytes, M., Linsky, J. L., et al.\ 2021, , 909, 61. doi:10.3847/1538-4357/abd62f

  80. [88]

    R., Sperauskas, J., & Boyle, R

    Upgren, A. R., Sperauskas, J., & Boyle, R. P.\ 2002, Baltic Astronomy, 11, 91

  81. [89]

    van der Walt, S., Colbert, S.C., Varoquaux, G., \ 2011, Computing in Science & Engineering, 13, 22

  82. [90]

    Virtanen, P., Gommers, R., Oliphant, T.E., et al., \ 2020, Nature Methods, 17, 261

  83. [91]

    A., Morgan, D

    West, A. A., Morgan, D. P., Bochanski, J. J., et al.\ 2011, , 141, 97. doi:10.1088/0004-6256/141/3/97

  84. [92]

    Woolf, V. M. & Wallerstein, G.\ 2005, , 356, 963. doi:10.1111/j.1365-2966.2004.08515.x

  85. [93]

    J., Newton, E

    Wright, N. J., Newton, E. R., Williams, P. K. G., et al.\ 2018, , 479, 2351. doi:10.1093/mnras/sty1670

  86. [94]

    Youngblood, A., France, K., Loyd, R. O. P., et al.\ 2017, , 843, 31. doi:10.3847/1538-4357/aa76dd

  87. [95]

    doi:10.3847/1538-4365/abd7a8

    Zhang, L.-Y., Meng, G., Long, L., et al.\ 2021, , 253, 19. doi:10.3847/1538-4365/abd7a8

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.