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REVIEW 2 major objections 6 minor 117 references

ALMA millimetre-wavelength imaging of HD 138965: New constraints on the debris dust composition and presence of planetary companions

T0 review · 2 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read First ALMA millimetre imaging of HD 138965 resolves its cool outer debris belt at 150 au and, combined with radiative-transfer modelling, limits any companion to 2.3 Jupiter masses.

desk verdict Credible first resolved mm view of HD 138965's outer belt; radius robust, width and composition model-dependent, but worth refereeing with fixes. read the letter →

arxiv 2506.11726 v1 pith:DE55ACKI submitted 2025-06-13 astro-ph.EP

classification astro-ph.EP
keywords debrisdiscscircumstellarmatterradiocontinuum:planetarysystemsplanet-discinteractionsstars:individual:HD138965millimetreastronomydustcompositionradiativetransfer
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 presents the first ALMA 1.3 mm observations to spatially resolve the cool outer debris belt around the young A star HD 138965. The belt peaks at $150^{+10}_{-7}$ au and is unusually broad, with a width of $49^{+7}_{-6}$ au, giving $\Delta R/R = 0.77$. The resolved architecture is then combined with a radiative-transfer model that moves dust grains under gravity, radiation pressure, and Poynting–Robertson drag; the best-fitting dust composition is astronomical silicate, while mixtures with 10% water ice remain plausible and 50% ice or any carbon mix is excluded. The same geometry yields an optical albedo limit of $\omega \le 0.09$ and a companion mass limit of $2.3 \pm 0.4$ Jupiter masses interior to 78 au. These results matter because a resolved debris belt's width and emission are among the few observable probes of unseen planets and the material composition of planet-forming regions.

What carries the argument

The argument runs through three pieces of machinery. First, a Gaussian-belt model of the ALMA visibilities, generated with a radiative-transfer code and sampled by a Markov chain Monte Carlo ensemble, yields the belt's radius, width, and orientation. Second, the paper's Stardust model redistributes dust grains under gravity, radiation pressure, Poynting–Robertson drag, and a simple collisional lifetime, predicting the spectral energy distribution for each grain composition, with model comparison by the Bayesian Information Criterion. Third, a literature single-planet sculpting relation converts the measured belt inner edge into a companion mass limit, and an established albedo formula turns the HST scattered-light non-detection into $\omega \le 0.09$.

What would settle it

Spatially resolving the inner belt would settle the question: JWST/MIRI imaging at about $10$ $\mu$m or longer-baseline ALMA at 1.3 mm could reach the ~0.25 arcsec separation of a 20 au inner belt, and showing its radius, width, or mass differs from the assumed values would directly test whether the outer-belt silicate preference is an artefact of the fixed inner-belt model. A second check is detecting water-ice or carbon spectral features in the outer belt's SED, since the model currently rules out 50% ice mixes and all tested carbon mixes.

Watch

Extended reading notes

Core claim

The central claim is that HD 138965's outer debris belt is spatially resolved at millimetre wavelengths for the first time, with a peak radius of $150^{+10}_{-7}$ au and a Gaussian width of $49^{+7}_{-6}$ au (fractional width $\Delta R/R = 0.77$), at an inclination of $49.9^{+3.3}_{-3.7}$ degrees. From this resolved structure, the authors find that astronomical silicate is the best-fitting dust composition for the outer belt among the tested inclusion mixtures, while scenarios with at least 10% crystalline or amorphous water ice cannot be rejected and 50% ice or any carbon mix is eliminated. Combining the ALMA image with the HST optical non-detection, they derive the scattering albedo upper limit $\omega \le 0.09$; and using a single-planet sculpting model on the belt's inner edge at 101 au, they place a companion mass limit of $2.3 \pm 0.4$ Jupiter masses for separations $a \le 78$ au.

Load-bearing premise

The ranking of outer-belt dust compositions depends on fixing the unresolved inner belt's radius ($13.7$ au), width ($1.75$ au), minimum grain size ($12$ $\mu$m), size distribution index ($q = 3.5$), and mass ($0.154 \times 10^{-3}$ Earth masses) from an initial Stardust fit that assumed the outer belt is pure astronomical silicate; if those inner-belt values are different, the BIC preference for silicate could change.

Editorial extensions

If this is right

  • The outer belt's fractional width of 0.77 places HD 138965 among the most radially extended resolved debris discs, comparable to HR 8799's belt, making it a candidate for hidden substructure that current data cannot resolve.
  • The companion mass limit of $2.3 \pm 0.4$ Jupiter masses interior to 78 au is about a factor of two tighter than the deepest direct-imaging limit at that separation, showing the power of belt morphology.
  • The non-detection in scattered light, combined with the resolved ALMA shape, yields an optical albedo upper limit of 0.09, which implies the disc would become detectable with roughly a factor of four deeper imaging.
  • The CO(2–1) upper limit of $2.7 \times 10^{-23}$ W m$^{-2}$ is about three times the level predicted for a gas mass of $8.6 \times 10^{-7}$ Earth masses, so gas is not ruled out but at present is unconstrained.

Reading between the lines

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

  • If the star's true age is closer to 350 Myr rather than the assumed Argus age of 31 Myr, the direct-imaging mass limits at 80 au weaken to roughly 15 Jupiter masses, which would make the architecture-based limit of 2.3 Jupiter masses the only meaningful constraint.
  • If future JWST/MIRI imaging places the inner belt at a radius different from the assumed 13.7 au, then the inner-belt grain temperature and mass would need to change to fit the same mid-infrared excess, which could shift the outer-belt composition preference.
  • The residual image shows a 2σ brightness asymmetry between the two ansae; if real, this could indicate an eccentric ring or pericentre glow, which deeper or higher-resolution ALMA imaging could confirm.
  • The discrepancy between the HST albedo limit (≤0.09) and the SED-inferred albedo (~0.56 for the smallest grains) suggests the outer-belt grains may be porous, icy, or strongly forward-scattering, which future polarimetric observations could test.
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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

2 major / 6 minor

Summary. The paper presents ALMA Band 6 millimetre-wavelength imaging of HD 138965, a young A star in the Argus association, and models the debris disc as a single Gaussian belt in the visibility plane. The authors report a resolved outer belt with peak radius 150^{+10}_{-7} au, width sigma_R = 49^{+7}_{-6} au (Delta R/R = 0.77), inclination 49.9 degrees, and position angle 173.3 degrees, with no constraint on the vertical scale height. They combine this geometry with an SED model (Stardust) to infer that astronomical silicate is the preferred outer-belt dust composition, while 10% water-ice inclusions cannot be excluded; from the HST non-detection they derive a scattering albedo limit omega <= 0.09; and from the belt architecture they place a companion mass limit of 2.3 +/- 0.4 M_Jup interior to about 78 au. They also report a non-detection of CO(2-1) emission.

Significance. If the reported geometry holds, this is the first millimetre-wavelength spatial resolution of the outer belt of HD 138965 and it anchors an independent dust-composition ranking, an albedo upper limit, and a companion-mass constraint that improves on direct imaging limits. The paper uses a standard and largely reproducible methodology (MCMC visibility fitting with RADMC-3D and Galario, BIC model comparison) and is appropriately explicit about several limiting assumptions, notably the unconstrained scale height and the degeneracy in the inner-belt parameters. The results are incremental rather than transformative, but they are a solid observational contribution to the debris-disc literature and the strengths of the analysis are the clear separation of the measurement (ALMA geometry) from the derived constraints (composition, albedo, companion mass).

major comments (2)
  1. [3.2, Table 2, Appendix B] The quoted width sigma_R = 49^{+7}_{-6} au is not robust against the unconstrained vertical scale height and the assumed single-Gaussian radial profile. The scale-height posterior is flat and uninformative (Appendix B), and Section 3.2 states that a broad range of scale heights fits the observations. At i ~= 50 degrees, a vertically thick belt can broaden the projected emission, and this broadening can be absorbed by sigma_R in a Gaussian model. Since sigma_R is subsequently used to set the outer-belt inner edge at R - sigma_R = 101 au for the companion limit (Section 4) and is fixed as an input to the SED modelling (Section 3.4), the width needs a robustness test: repeat the visibility fit with h fixed at the extremes (for example 0.01 and 0.30) and with an alternative radial profile (for example a sharp-edged ring or a power-law surface density) to establish that Delta R/R = 0.77 is not an artefact of the model family. The 2-sigma NW/SE residual asymmetry in Figure 1 reinforces the need for such a test.
  2. [3.4, Table 4] The outer-belt composition ranking is conditional on fixed inner-belt parameters (r_m = 13.7 au, sigma_r = 1.75 au, s_min = 12 micron, q = 3.5, M_s = 0.154 x 10^-3 M_Earth) that were obtained from an initial Stardust fit assuming a pure-silicate outer belt. The text explicitly acknowledges that this may bias the outer-belt comparison toward astronomical silicate, but the inner-belt parameters are not marginalised over or varied in the composition fits. The Delta BIC margins separating S100 from S90WC10 (7.0) and from S90WA10 (4.4) are modest, so a plausible change in the inner-belt parameters could alter the ranking. I request a sensitivity test in which the inner-belt radius, s_min, q, or mass are varied within their posterior ranges, or a joint fit, to show that the silicate preference is not an artefact of the initial assumption.
minor comments (6)
  1. [3.2, Table 2 vs Figure B1] The disc flux density is reported as 1.460 +/- 0.230 mJy in Table 2 but as 1.46^{+0.08}_{-0.07} mJy in Figure B1; if the larger uncertainty includes the weather-related calibration systematic described in Section 2.1, this should be stated explicitly, since the SED fit uses the +/- 0.23 mJy value.
  2. [3.4, Table 4] The initial MCMC analysis is reported in the text to give an inner-belt mean radius of 15^{+3}_{-2} au, while Table 4 lists r_m = 13.7 au; please state which value (posterior median or maximum likelihood) was adopted for the fixed inner-belt model.
  3. [3.4, Table 5, Abstract] The sentence in Section 3.4 saying that the best-fit results correspond to the porosity scenarios and to a strong candidate with 90:10 crystalline water ice appears to contradict the BIC table and the abstract, which identify pure astronomical silicate as the best fit; please reconcile the wording.
  4. [Abstract, Section 4] The companion mass limit is quoted as a <= 78 au in the abstract but as a <= 74^{+8}_{-6} au in Section 4, and the improvement factor is stated as a factor of two in the abstract but as a factor of five over Matthews et al. (2018) and a factor of two over the SHARDDS image in Section 4; unify these numbers and specify the baseline.
  5. [3.5, Table 5] The SED-derived albedo of 0.56 is computed for 'dirty ice' grains at 1.6 micron, whereas the preferred composition in Table 5 is pure astronomical silicate and the HST limit is at 0.6 micron; recompute the comparison at the HST wavelength for the best-fit composition, or present it explicitly as an illustrative consistency check rather than as a tension.
  6. [Appendix B] The caption says the posterior distributions are based on '10,0000 realisations', which appears to be a typo, and Figure B1 omits the scale-height posterior; since h is unconstrained, please show at least the h-sigma_R covariance or state explicitly that it was not stored.

Circularity Check

1 steps flagged · score 3.0 of 10

ALMA geometry, HST albedo limit, and companion mass limit are self-contained; the only circular chain is the acknowledged coupling between the assumed outer-belt silicate composition and the fixed inner-belt parameters used in the SED composition ranking.

  1. fitted input called prediction [Section 3.4 (SED modelling), Tables 4 and 5; see also Section 4 Discussion]
    "We performed an initial analysis using Stardust to estimate the inner belt characteristics. ... To pin down the radial component of the inner belt, we ran Stardust with the following assumptions for the inner belt: minimum grain size of 12 μm, size distribution component to be 3.5 ... and the disc fractional width (Δr/r) to be 0.3. ... It should be noted that the emission from the inner belt is fixed following the initial modelling where the outer belt grains are assumed to be solely astronomical silicate."

    The outer-belt composition ranking (Table 5) is evaluated with the inner-belt parameters (r_m = 13.7 au, σ_r = 1.75 au, s_min = 12 μm, q = 3.5, M_s = 0.154×10^-3 M_Earth; Table 4) held fixed. Those parameters were obtained from an initial Stardust SED fit that assumed the outer belt is pure astronomical silicate. The BIC comparison then ranks pure astronomical silicate as the best outer-belt composition using an inner-belt model that was itself tuned under exactly that composition hypothesis. The paper explicitly acknowledges the bias but does not marginalize over the inner-belt parameters, so the composition ranking is partly an input: the fixed inner belt is not composition-neutral.

full rationale

The core empirical claims are self-contained. The ALMA visibility fit measures R_peak = 150+10/-7 au and σ_R = 49+7/-6 au directly from the data (Section 3.2, Table 2), independent of any composition or companion model. The HST-based albedo limit ω ≤ 0.09 uses an external non-detection and the measured disc geometry, not the SED fit. The companion mass limit is a forward application of Pearce et al. (2022) stirring/sculpting models to the measured inner edge R - σ_R = 101 au. The one genuine circular chain is in the dust-composition analysis: the inner-belt parameters are fixed using an initial Stardust fit that assumes the outer belt is pure astronomical silicate, and those fixed parameters are then used in the BIC ranking that selects pure astronomical silicate for the outer belt. The authors flag this bias in Section 3.4 and again in Section 4, but the ranking is nevertheless not fully independent. The unconstrained vertical scale height and single-Gaussian profile are model-assumption concerns that affect the robustness of the width measurement, but they are not circularity: the width is an empirical fit, and using that fit as an input to later SED and companion calculations is standard practice rather than an identity. Overall, the central ALMA measurement and the derived albedo and companion limits stand on their own; only the composition ranking carries a moderate, acknowledged coupling, giving a score of 3.

Assumptions & free parameters 12 free parameters · 8 assumptions · 0 invented entities

The central measurement (belt radius, width) rests on a standard visibility fit with a handful of geometric parameters. The composition and companion claims rest on several imposed assumptions: fixed inner belt parameters from a coupled fit, a power-law grain size distribution capped at 3 mm, an assumed stellar age of 31 Ma, and a single-planet stirring model. No new entities are introduced.

free parameters (12)
  • Outer belt peak radius R_peak = 150 +10/-7 au
    Fitted to ALMA visibilities; central to the resolved architecture claim (Table 2).
  • Outer belt width sigma_R = 49 +7/-6 au
    Fitted to ALMA visibilities; central to the broad-belt claim (Table 2).
  • Outer belt inclination i = 49.9 +3.3/-3.7 deg
    Fitted to visibilities; used for albedo and companion interpretations.
  • Position angle phi = 173.3 +3.7/-4.1 deg
    Fitted to visibilities; defines belt orientation on sky.
  • Outer belt flux density f_disc = 1.46 ± 0.23 mJy
    Fitted flux; input to SED modeling.
  • Inner belt radius = 13.7 au
    Fixed after initial Stardust fit; load-bearing for composition comparison.
  • Inner belt width = 1.75 au
    Fixed after initial Stardust fit; load-bearing for composition comparison.
  • Inner belt minimum grain size = 12 μm
    Fixed after initial Stardust fit; affects inner belt SED.
  • Inner belt size distribution exponent q = 3.5
    Assumed steady-state collisional cascade; not fitted.
  • Inner belt dust mass = 0.154 ×10^-3 M_Earth
    Fixed after initial Stardust fit (Table 4).
  • Outer belt size distribution exponent q = 3.49 to 4.89 depending on composition
    Fitted to SED for each composition scenario (Table 5).
  • Outer belt dust mass = 3.18 to 9.20 ×10^-3 M_Earth
    Fitted to SED for each composition scenario (Table 5).
assumptions (8)
  • standard math Grains are spherical and emit/absorb according to Mie theory
    Appendix A; standard for dust modeling but an idealization.
  • domain assumption The debris disc is optically thin and gas-free
    Appendix A; supported by CO non-detection (3.3), but assumes no gas affects dynamics.
  • domain assumption Grain size distribution is a power law with index q between 2 and 5
    Section 3.4; standard for debris discs.
  • ad hoc to paper Maximum grain size is 3000 μm
    Section 3.4; chosen to bound the size distribution and affects dust mass.
  • ad hoc to paper The inner belt parameters derived from an initial fit with pure-silicate outer belt are correct
    Section 3.4 and Table 4; acknowledged bias in comparing outer belt compositions.
  • domain assumption The star is young with age 31 ± 11 Ma
    Section 3.1; derived with a pre-main-sequence prior; without it, age is 805 ± 468 Ma.
  • domain assumption The Pearce et al. (2022) single-planet stirring model applies
    Section 4; used to convert belt architecture to companion mass limit.
  • domain assumption Fractional luminosity is a proxy for optical depth in the collisional lifetime calculation
    Section 3.4 and Appendix A; standard approximation.

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Cite this review

Pith. "Pith review of ALMA millimetre-wavelength imaging of HD 138965: New constraints on the debris dust composition and presence of planetary companions." pith.science (2026). https://pith.science/paper/DE55ACKI

@misc{pith2026250611726,
  author       = {Pith},
  title        = {Pith review of: ALMA millimetre-wavelength imaging of HD 138965: New constraints on the debris dust composition and presence of planetary companions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DE55ACKI}},
  note         = {Machine review of arXiv:2506.11726}
}
abstract

HD 138965 is a young A type star and member of the nearby young Argus association. This star is surrounded by a broad, bright debris disc with two temperature components that was spatially resolved at far-infrared wavelengths by Herschel. Here we present ALMA millimetre-wavelength imaging of the cool outer belt. These reveal its radial extent to be $150^{+10}_{-7}$ au with a width ($\sigma$) of 49$^{+7}_{-6}$ au (${\Delta}R/R$ = 0.77), at a moderate inclination of 49$\fdeg$9^{+3.3}_{-3.7}$. Due to the limited angular resolution, signal-to-noise, and inclination we have no constraint on the disc's vertical scale height. We modelled the disc emission with both gravitational and radiation forces acting on the dust grains. As the inner belt has not been spatially resolved, we fixed its radius and width prior to modelling the outer belt. We find astronomical silicate is the best fit for the dust composition. However, we could not reject possible scenarios where there are at least 10 \% water-ice inclusions. Combining the spatially resolved imaging by ALMA with non-detection at optical wavelengths by HST, we obtain a limit on the scattering albedo $\omega \leq 0.09$ for the debris dust in the outer belt. Analysis of the outer belt's architecture in conjunction with simple stirring models places a mass limit of $2.3~\pm~0.4 M_{\rm Jup}$ on a companion interior to the belt ($a \leq 78$ au), a factor of two improvement over constraints from high contrast imaging.

Figures

Figures reproduced from arXiv: 2506.11726 by the authors.

Figure 1
Figure 1. Left: ALMA Band 6 continuum image of HD 138965. The image has been cleaned and reconstructed with a Briggs weight of 0.5. The instrument beam (1. ′′39 × 1. ′′17, 𝜙 = 10.5 ◦ ) is denoted by the white ellipse in the bottom left corner. Contours are in steps of 2-𝜎 from +2-𝜎. Orientation is north up, east left. Right: Residuals after subtraction of the maximum likelihood model from the observations. We see there two fe… view at source ↗
Figure 2
Figure 2. ALMA CO spectrum centred on a rest frequency of 230.538 GHz. The blue shaded region denotes the 10 km/s wide channel used to determine the presence, or rather absence, of significant CO emission, centred on the stellar radial velocity from Gaia DR3 (i.e. -9.38 km/s). Uncertainties in the unbinned spectrum channels (1.3 km/s wide) are denoted by the blue vertical lines. The grey horizontal dashed lines denote the unc… view at source ↗
Figure 3
Figure 3. Spectral energy distribution of HD 138965 for the scenario where grains subjected to forces of gravity, radiation pressure, and drag induced by the Poynting-Robertson effect. The inner and outer belts were composed of 90:10 astronomical silicate:amorphous water-ice. White data points are literature photometry, except for the ALMA data point. The light blue line denotes the Spitzer/IRS spectrum. The stellar photosphe… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Mass limits from VLT/SPHERE SHARDDS observation of HD 138965 based on AMES-COND evolutionary models (Allard et al. 2001). Contrast as a function of separation was calculated using the RSM algo￾rithm as presented in Dahlqvist et al. (2022). The dot (green), dash (orange…

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Works this paper leans on

117 extracted references · 27 canonical work pages

  1. [1]

    H., Alexander D

    Allard F., Hauschildt P. H., Alexander D. R., Tamanai A., Schweitzer A., 2001, @doi [ ] 10.1086/321547 , https://ui.adsabs.harvard.edu/abs/2001ApJ...556..357A 556, 357

  2. [2]

    Allard F., Homeier D., Freytag B., 2012, @doi [Philosophical Transactions of the Royal Society of London Series A] 10.1098/rsta.2011.0269 , https://ui.adsabs.harvard.edu/abs/2012RSPTA.370.2765A 370, 2765

  3. [3]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/#abs/2013A&A...558A..33A 558, A33

  4. [4]

    Astropy Collaboration et al., 2018, @doi [ ] 10.3847/1538-3881/aabc4f , http://adsabs.harvard.edu/abs/2018AJ....156..123A 156, 123

  5. [5]

    Astropy Collaboration et al., 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

  6. [6]

    C., Beust H., 2006, @doi [ ] 10.1051/0004-6361:20054250 , https://ui.adsabs.harvard.edu/abs/2006A&A...455..987A 455, 987

    Augereau J. C., Beust H., 2006, @doi [ ] 10.1051/0004-6361:20054250 , https://ui.adsabs.harvard.edu/abs/2006A&A...455..987A 455, 987

  7. [7]

    Babusiaux C., et al., 2023, @doi [ ] 10.1051/0004-6361/202243790 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A..32B 674, A32

  8. [8]

    A., Neugebauer G., Habing H

    Beichman C. A., Neugebauer G., Habing H. J., Clegg P. E., Chester T. J., eds, 1988, Infrared Astronomical Satellite (IRAS) Catalogs and Atlases.Volume 1: Explanatory Supplement. 1 Vol. 1

Show all 117 references
  1. [9]

    L., et al., 2019, @doi [ ] 10.1051/0004-6361/201935251 , https://ui.adsabs.harvard.edu/abs/2019A&A...631A.155B 631, A155

    Beuzit J. L., et al., 2019, @doi [ ] 10.1051/0004-6361/201935251 , https://ui.adsabs.harvard.edu/abs/2019A&A...631A.155B 631, A155

  2. [10]

    F., Huffman D

    Bohren C. F., Huffman D. R., 1998, Absorption and Scattering of Light by Small Particles . John Wiley & Sons

  3. [11]

    arXiv:2407.01413

    Booth M., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2407.01413 , https://ui.adsabs.harvard.edu/abs/2024arXiv240701413B p. arXiv:2407.01413

  4. [12]

    Bressan A., Marigo P., Girardi L., Salasnich B., Dal Cero C., Rubele S., Nanni A., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21948.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..127B 427, 127

  5. [13]

    P., Anderson D

    Burnham K. P., Anderson D. R., 2004, @doi [Sociological Methods & Research] 10.1177/0049124104268644 , 33, 261

  6. [14]

    A., Lamy P

    Burns J. A., Lamy P. L., Soter S., 1979, @doi [ ] 10.1016/0019-1035(79)90050-2 , http://adsabs.harvard.edu/abs/1979Icar...40....1B 40, 1

  7. [15]

    H., Mittal T., Kuchner M., Forrest W

    Chen C. H., Mittal T., Kuchner M., Forrest W. J., Lisse C. M., Manoj P., Sargent B. A., Watson D. M., 2014a, @doi [ ] 10.1088/0067-0049/211/2/25 , https://ui.adsabs.harvard.edu/abs/2014ApJS..211...25C 211, 25

  8. [16]

    Chen Y., Girardi L., Bressan A., Marigo P., Barbieri M., Kong X., 2014b, @doi [ ] 10.1093/mnras/stu1605 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.2525C 444, 2525

  9. [17]

    Chen Y., Bressan A., Girardi L., Marigo P., Kong X., Lanza A., 2015, @doi [ ] 10.1093/mnras/stv1281 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.1068C 452, 1068

  10. [18]

    Choquet \'E ., et al., 2017, @doi [ ] 10.3847/2041-8213/834/2/L12 , http://adsabs.harvard.edu/abs/2017ApJ...834L..12C 834, L12

  11. [19]

    Choquet \'E ., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa892 , https://ui.adsabs.harvard.edu/abs/2018ApJ...854...53C 854, 53

  12. [20]

    F., et al., 2021, @doi [ ] 10.1093/mnras/stab1237 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.4497C 504, 4497

    Cronin-Coltsmann P. F., et al., 2021, @doi [ ] 10.1093/mnras/stab1237 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.4497C 504, 4497

  13. [21]

    F., Kennedy G

    Cronin-Coltsmann P. F., Kennedy G. M., Kral Q., Lestrade J.-F., Marino S., Matr \`a L., Wyatt M. C., 2023, @doi [ ] 10.1093/mnras/stad3083 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.5401C 526, 5401

  14. [22]

    A., et al., 2024, @doi [ ] 10.3847/1538-4357/ad0e69 , https://ui.adsabs.harvard.edu/abs/2024ApJ...961..245C 961, 245

    Crotts K. A., et al., 2024, @doi [ ] 10.3847/1538-4357/ad0e69 , https://ui.adsabs.harvard.edu/abs/2024ApJ...961..245C 961, 245

  15. [23]

    H., et al., 2022, @doi [ ] 10.1051/0004-6361/202244145 , https://ui.adsabs.harvard.edu/abs/2022A&A...666A..33D 666, A33

    Dahlqvist C. H., et al., 2022, @doi [ ] 10.1051/0004-6361/202244145 , https://ui.adsabs.harvard.edu/abs/2022A&A...666A..33D 666, A33

  16. [24]

    Daley C., et al., 2019, @doi [ ] 10.3847/1538-4357/ab1074 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875...87D 875, 87

  17. [25]

    J., Hillenbrand L

    David T. J., Hillenbrand L. A., 2015, @doi [ ] 10.1088/0004-637X/804/2/146 , https://ui.adsabs.harvard.edu/abs/2015ApJ...804..146D 804, 146

  18. [26]

    S., 1969, @doi [ ] 10.1029/JB074i010p02531 , http://adsabs.harvard.edu/abs/1969JGR....74.2531D 74, 2531

    Dohnanyi J. S., 1969, @doi [ ] 10.1029/JB074i010p02531 , http://adsabs.harvard.edu/abs/1969JGR....74.2531D 74, 2531

  19. [27]

    T., 2003, @doi [ ] 10.1086/379118 , http://adsabs.harvard.edu/abs/2003ApJ...598.1017D 598, 1017

    Draine B. T., 2003, @doi [ ] 10.1086/379118 , http://adsabs.harvard.edu/abs/2003ApJ...598.1017D 598, 1017

  20. [28]

    Dullemond C. P., Juhasz A., Pohl A., Sereshti F., Shetty R., Peters T., Commercon B., Flock M., 2012, RADMC-3D: A multi-purpose radiative transfer tool , Astrophysics Source Code Library, record ascl:1202.015

  21. [29]

    Eiroa C., et al., 2013, @doi [ ] 10.1051/0004-6361/201321050 , https://ui.adsabs.harvard.edu/abs/2013A&A...555A..11E 555, A11

  22. [30]

    M., et al., 2020, @doi [ ] 10.3847/1538-3881/ab9199 , https://ui.adsabs.harvard.edu/abs/2020AJ....160...24E 160, 24

    Esposito T. M., et al., 2020, @doi [ ] 10.3847/1538-3881/ab9199 , https://ui.adsabs.harvard.edu/abs/2020AJ....160...24E 160, 24

  23. [31]

    Faramaz V., et al., 2021, @doi [ ] 10.3847/1538-3881/abf4e0 , https://ui.adsabs.harvard.edu/abs/2021AJ....161..271F 161, 271

  24. [32]

    Foreman-Mackey D., 2016, @doi [The Journal of Open Source Software] 10.21105/joss.00024 , 24

  25. [33]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

  26. [34]

    Fouesneau M., 2022, "pyphot", https://github.com/mfouesneau/pyphot

  27. [35]

    Gagn \'e J., et al., 2018, @doi [ ] 10.3847/1538-4357/aaae09 , https://ui.adsabs.harvard.edu/abs/2018ApJ...856...23G 856, 23

  28. [36]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629272 , https://ui.adsabs.harvard.edu/abs/2016A&A...595A...1G 595, A1

  29. [37]

    Gaia Collaboration et al., 2023, @doi [ ] 10.1051/0004-6361/202243940 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...1G 674, A1

  30. [38]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

  31. [39]

    M., Eiroa C., del Burgo C., Marshall J

    Heras A. M., Eiroa C., del Burgo C., Marshall J. P., Montesinos B., 2025, @doi [ ] 10.1051/0004-6361/202449826 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A.325H 694, A325

  32. [40]

    H g E., et al., 2000, , https://ui.adsabs.harvard.edu/abs/2000A&A...355L..27H 355, L27

  33. [41]

    S., et al., 2017, @doi [ ] 10.1093/mnras/stx1378 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.3606H 470, 3606

    Holland W. S., et al., 2017, @doi [ ] 10.1093/mnras/stx1378 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.3606H 470, 3606

  34. [42]

    Horner J., et al., 2020, @doi [ ] 10.1088/1538-3873/ab8eb9 , https://ui.adsabs.harvard.edu/abs/2020PASP..132j2001H 132, 102001

  35. [43]

    M., Sandford S

    Hudgins D. M., Sandford S. A., Allamandola L. J., Tielens A. G. G. M., 1993, @doi [ ] 10.1086/191796 , https://ui.adsabs.harvard.edu/abs/1993ApJS...86..713H 86, 713

  36. [44]

    M., Duch \^e ne G., Matthews B

    Hughes A. M., Duch \^e ne G., Matthews B. C., 2018, @doi [ ] 10.1146/annurev-astro-081817-052035 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..541H 56, 541

  37. [45]

    D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/#abs/2007CSE.....9...90H 9, 90

    Hunter J. D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/#abs/2007CSE.....9...90H 9, 90

  38. [46]

    Ishihara D., et al., 2010, @doi [ ] 10.1051/0004-6361/200913811 , https://ui.adsabs.harvard.edu/abs/2010A&A...514A...1I 514, A1

  39. [47]

    C., Greaves J

    Kains N., Wyatt M. C., Greaves J. S., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18566.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.414.2486K 414, 2486

  40. [48]

    J., et al., 2009, @doi [ ] 10.1088/0004-6256/137/6/4917 , https://ui.adsabs.harvard.edu/abs/2009AJ....137.4917K 137, 4917

    Kavelaars J. J., et al., 2009, @doi [ ] 10.1088/0004-6256/137/6/4917 , https://ui.adsabs.harvard.edu/abs/2009AJ....137.4917K 137, 4917

  41. [49]

    M., Wyatt M

    Kennedy G. M., Wyatt M. C., 2013, @doi [ ] 10.1093/mnras/stt900 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.433.2334K 433, 2334

  42. [50]

    M., et al., 2018, @doi [ ] 10.1093/mnras/sty492 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.4584K 476, 4584

    Kennedy G. M., et al., 2018, @doi [ ] 10.1093/mnras/sty492 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.4584K 476, 4584

  43. [51]

    Klaassen P., et al., 2024, @doi [Open Research Europe] 10.12688/openreseurope.17450.1 , https://ui.adsabs.harvard.edu/abs/2024ORE.....4..112K 4, 112

  44. [52]

    Kla c ka J., Kocifaj M., P \'a stor P., Petr z ala J., 2007, @doi [ ] 10.1051/0004-6361:20066132 , https://ui.adsabs.harvard.edu/abs/2007A&A...464..127K 464, 127

  45. [53]

    J., 2021, @doi [ ] 10.1016/j.icarus.2021.114613 , https://ui.adsabs.harvard.edu/abs/2021Icar..36814613K 368, 114613

    Kossacki K. J., 2021, @doi [ ] 10.1016/j.icarus.2021.114613 , https://ui.adsabs.harvard.edu/abs/2021Icar..36814613K 368, 114613

  46. [54]

    C., Kennedy G

    Kral Q., Matr \`a L., Wyatt M. C., Kennedy G. M., 2017, @doi [ ] 10.1093/mnras/stx730 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469..521K 469, 521

  47. [55]

    E., et al., 2010, @doi [ ] 10.1088/0004-6256/140/4/1051 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1051K 140, 1051

    Krist J. E., et al., 2010, @doi [ ] 10.1088/0004-6256/140/4/1051 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1051K 140, 1051

  48. [56]

    V., 2010, @doi [Research in Astronomy and Astrophysics] 10.1088/1674-4527/10/5/001 , http://adsabs.harvard.edu/abs/2010RAA....10..383K 10, 383

    Krivov A. V., 2010, @doi [Research in Astronomy and Astrophysics] 10.1088/1674-4527/10/5/001 , http://adsabs.harvard.edu/abs/2010RAA....10..383K 10, 383

  49. [57]

    V., Booth M., 2018, @doi [ ] 10.1093/mnras/sty1607 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.3300K 479, 3300

    Krivov A. V., Booth M., 2018, @doi [ ] 10.1093/mnras/sty1607 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.3300K 479, 3300

  50. [58]

    V., Wyatt M

    Krivov A. V., Wyatt M. C., 2021, @doi [ ] 10.1093/mnras/staa2385 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..718K 500, 718

  51. [59]

    u ller S., L \

    Krivov A. V., M \"u ller S., L \"o hne T., Mutschke H., 2008, @doi [ ] 10.1086/591507 , http://adsabs.harvard.edu/abs/2008ApJ...687..608K 687, 608

  52. [60]

    J., Stark C

    Kuchner M. J., Stark C. C., 2010, @doi [ ] 10.1088/0004-6256/140/4/1007 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1007K 140, 1007

  53. [61]

    J., Spoon H

    Lebouteiller V., Barry D. J., Spoon H. W. W., Bernard-Salas J., Sloan G. C., Houck J. R., Weedman D. W., 2011, @doi [ ] 10.1088/0067-0049/196/1/8 , https://ui.adsabs.harvard.edu/abs/2011ApJS..196....8L 196, 8

  54. [62]

    F., Wyatt M

    Lestrade J. F., Wyatt M. C., Bertoldi F., Menten K. M., Labaigt G., 2009, @doi [ ] 10.1051/0004-6361/200912306 , https://ui.adsabs.harvard.edu/abs/2009A&A...506.1455L 506, 1455

  55. [63]

    Liu Q., Wang T., Jiang P., 2014, @doi [ ] 10.1088/0004-6256/148/1/3 , https://ui.adsabs.harvard.edu/abs/2014AJ....148....3L 148, 3

  56. [64]

    V., Booth M., Lestrade J.-F., 2020, @doi [ ] 10.1093/mnras/staa2608 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3932L 499, 3932

    Luppe P., Krivov A. V., Booth M., Lestrade J.-F., 2020, @doi [ ] 10.1093/mnras/staa2608 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3932L 499, 3932

  57. [65]

    A., et al., 2016, @doi [ ] 10.3847/0004-637X/823/2/79 , https://ui.adsabs.harvard.edu/abs/2016ApJ...823...79M 823, 79

    MacGregor M. A., et al., 2016, @doi [ ] 10.3847/0004-637X/823/2/79 , https://ui.adsabs.harvard.edu/abs/2016ApJ...823...79M 823, 79

  58. [66]

    Marino S., 2021, @doi [ ] 10.1093/mnras/stab771 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.5100M 503, 5100

  59. [67]

    P., et al., 2014, @doi [ ] 10.1051/0004-6361/201424517 , https://ui.adsabs.harvard.edu/abs/2014A&A...570A.114M 570, A114

    Marshall J. P., et al., 2014, @doi [ ] 10.1051/0004-6361/201424517 , https://ui.adsabs.harvard.edu/abs/2014A&A...570A.114M 570, A114

  60. [68]

    P., Maddison S

    Marshall J. P., Maddison S. T., Thilliez E., Matthews B. C., Wilner D. J., Greaves J. S., Holland W. S., 2017, @doi [ ] 10.1093/mnras/stx645 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.2719M 468, 2719

  61. [69]

    P., Milli J., Choquet \'E ., del Burgo C., Kennedy G

    Marshall J. P., Milli J., Choquet \'E ., del Burgo C., Kennedy G. M., Matr \`a L., Ertel S., Boccaletti A., 2018, @doi [ ] 10.3847/1538-4357/aaec6a , https://ui.adsabs.harvard.edu/abs/2018ApJ...869...10M 869, 10

  62. [70]

    P., Wang L., Kennedy G

    Marshall J. P., Wang L., Kennedy G. M., Zeegers S. T., Scicluna P., 2021, @doi [ ] 10.1093/mnras/staa3917 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.6168M 501, 6168

  63. [71]

    P., et al., 2023a, @doi [ ] 10.1093/mnras/stad913 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.5940M 521, 5940

    Marshall J. P., et al., 2023a, @doi [ ] 10.1093/mnras/stad913 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.5940M 521, 5940

  64. [72]

    P., Cotton D

    Marshall J. P., Cotton D. V., Bott K., Bailey J., Kedziora-Chudczer L., Brown E. L., 2023b, @doi [ ] 10.1093/mnras/stad979 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.2777M 522, 2777

  65. [73]

    Matr \`a L., et al., 2025, @doi [ ] 10.1051/0004-6361/202451397 , https://ui.adsabs.harvard.edu/abs/2025A&A...693A.151M 693, A151

  66. [74]

    Matthews E., et al., 2018, @doi [ ] 10.1093/mnras/sty1778 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.2757M 480, 2757

  67. [75]

    Montesinos B., et al., 2016, @doi [ ] 10.1051/0004-6361/201628329 , https://ui.adsabs.harvard.edu/abs/2016A&A...593A..51M 593, A51

  68. [76]

    Y., et al., 2009, @doi [ ] 10.1088/0004-637X/699/2/1067 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699.1067M 699, 1067

    Morales F. Y., et al., 2009, @doi [ ] 10.1088/0004-637X/699/2/1067 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699.1067M 699, 1067

  69. [77]

    Y., Bryden G., Werner M

    Morales F. Y., Bryden G., Werner M. W., Stapelfeldt K. R., 2016, @doi [ ] 10.3847/0004-637X/831/1/97 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831...97M 831, 97

  70. [78]

    A., Marshall J

    Mu \ n oz-Guti \'e rrez M. A., Marshall J. P., Peimbert A., 2023, @doi [ ] 10.1093/mnras/stad218 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.3218M 520, 3218

  71. [79]

    D., Min M., Dominik C., Debes J

    Mulders G. D., Min M., Dominik C., Debes J. H., Schneider G., 2013, @doi [ ] 10.1051/0004-6361/201219522 , https://ui.adsabs.harvard.edu/abs/2013A&A...549A.112M 549, A112

  72. [80]

    u ller S., L \

    M \"u ller S., L \"o hne T., Krivov A. V., 2010, @doi [ ] 10.1088/0004-637X/708/2/1728 , https://ui.adsabs.harvard.edu/abs/2010ApJ...708.1728M 708, 1728

  73. [81]

    J., Wyatt M

    Mustill A. J., Wyatt M. C., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15360.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.399.1403M 399, 1403

  74. [82]

    R., Kenyon S

    Najita J. R., Kenyon S. J., Bromley B. C., 2022, @doi [ ] 10.3847/1538-4357/ac37b6 , https://ui.adsabs.harvard.edu/abs/2022ApJ...925...45N 925, 45

  75. [83]

    J., et al., 2021, @doi [ ] 10.1093/mnras/stab1901 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.3139N 507, 3139

    Norfolk B. J., et al., 2021, @doi [ ] 10.1093/mnras/stab1901 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.3139N 507, 3139

  76. [84]

    E., 2012, @doi [ ] 10.1088/0004-637X/747/2/113 , https://ui.adsabs.harvard.edu/abs/2012ApJ...747..113P 747, 113

    Pan M., Schlichting H. E., 2012, @doi [ ] 10.1088/0004-637X/747/2/113 , https://ui.adsabs.harvard.edu/abs/2012ApJ...747..113P 747, 113

  77. [85]

    M., Willson L

    Patten B. M., Willson L. A., 1991, @doi [ ] 10.1086/115879 , https://ui.adsabs.harvard.edu/abs/1991AJ....102..323P 102, 323

  78. [86]

    V., Marshall J

    Pawellek N., Krivov A. V., Marshall J. P., Montesinos B., \'A brah \'a m P., Mo \'o r A., Bryden G., Eiroa C., 2014, @doi [ ] 10.1088/0004-637X/792/1/65 , https://ui.adsabs.harvard.edu/abs/2014ApJ...792...65P 792, 65

  79. [87]

    D., et al., 2022, @doi [ ] 10.1051/0004-6361/202142720 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A.135P 659, A135

    Pearce T. D., et al., 2022, @doi [ ] 10.1051/0004-6361/202142720 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A.135P 659, A135

  80. [88]

    M., et al., 2011, @doi [ ] 10.1088/0004-6256/142/4/131 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..131P 142, 131

    Petit J. M., et al., 2011, @doi [ ] 10.1088/0004-6256/142/4/131 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..131P 142, 131

  81. [89]

    H., 1903, , https://ui.adsabs.harvard.edu/abs/1903MNRAS..64A...1P 64, 1

    Poynting J. H., 1903, , https://ui.adsabs.harvard.edu/abs/1903MNRAS..64A...1P 64, 1

  82. [90]

    W., Henning T., 1993, , https://ui.adsabs.harvard.edu/abs/1993A&A...279..577P 279, 577

    Preibisch T., Ossenkopf V., Yorke H. W., Henning T., 1993, , https://ui.adsabs.harvard.edu/abs/1993A&A...279..577P 279, 577

  83. [91]

    B., et al., 2023, @doi [ ] 10.1051/0004-6361/202245458 , https://ui.adsabs.harvard.edu/abs/2023A&A...672A.114R 672, A114

    Ren B. B., et al., 2023, @doi [ ] 10.1051/0004-6361/202245458 , https://ui.adsabs.harvard.edu/abs/2023A&A...672A.114R 672, A114

  84. [92]

    H., Song I., Zuckerman B., McElwain M., 2007, @doi [ ] 10.1086/509912 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660.1556R 660, 1556

    Rhee J. H., Song I., Zuckerman B., McElwain M., 2007, @doi [ ] 10.1086/509912 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660.1556R 660, 1556

  85. [93]

    Riello M., et al., 2021, @doi [ ] 10.1051/0004-6361/202039587 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A...3R 649, A3

  86. [94]

    P., 1937, @doi [ ] 10.1093/mnras/97.6.423 , https://ui.adsabs.harvard.edu/abs/1937MNRAS..97..423R 97, 423

    Robertson H. P., 1937, @doi [ ] 10.1093/mnras/97.6.423 , https://ui.adsabs.harvard.edu/abs/1937MNRAS..97..423R 97, 423

  87. [95]

    Schwarz G., 1978, @doi [The Annals of Statistics] 10.1214/aos/1176344136 , 6, 461

  88. [96]

    M., Wyatt M

    Sibthorpe B., Kennedy G. M., Wyatt M. C., Lestrade J. F., Greaves J. S., Matthews B. C., Duch \^e ne G., 2018, @doi [ ] 10.1093/mnras/stx3188 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.3046S 475, 3046

  89. [97]

    F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

    Skrutskie M. F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

  90. [98]

    Tang J., Bressan A., Rosenfield P., Slemer A., Marigo P., Girardi L., Bianchi L., 2014, @doi [ ] 10.1093/mnras/stu2029 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.4287T 445, 4287

  91. [99]

    Tazzari M., Beaujean F., Testi L., 2018, @doi [ ] 10.1093/mnras/sty409 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.4527T 476, 4527

  92. [100]

    D., et al., 2014, @doi [ ] 10.1093/mnras/stu1864 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.2558T 445, 2558

    Thureau N. D., et al., 2014, @doi [ ] 10.1093/mnras/stu1864 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.2558T 445, 2558

  93. [101]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

  94. [102]

    V., Kobayashi H., L \"o hne T., 2012, @doi [ ] 10.1051/0004-6361/201118551 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..30V 540, A30

    Vitense C., Krivov A. V., Kobayashi H., L \"o hne T., 2012, @doi [ ] 10.1051/0004-6361/201118551 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..30V 540, A30

  95. [103]

    Wahhaj Z., et al., 2016, @doi [ ] 10.1051/0004-6361/201321887 , http://cdsads.u-strasbg.fr/abs/2016arXiv161105866W 596, L4

  96. [104]

    G., Brandt R

    Warren S. G., Brandt R. E., 2008, @doi [Journal of Geophysical Research (Atmospheres)] 10.1029/2007JD009744 , https://ui.adsabs.harvard.edu/abs/2008JGRD..11314220W 113, D14220

  97. [105]

    Wenger M., et al., 2000, @doi [ ] 10.1051/aas:2000332 , https://ui.adsabs.harvard.edu/abs/2000A&AS..143....9W 143, 9

  98. [106]

    A., 2003, @doi [ ] 10.1086/377638 , http://adsabs.harvard.edu/abs/2003ApJ...596..603W 596, 603

    Wolf S., Hillenbrand L. A., 2003, @doi [ ] 10.1086/377638 , http://adsabs.harvard.edu/abs/2003ApJ...596..603W 596, 603

  99. [107]

    L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

    Wright E. L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

  100. [108]

    S., et al., 2023, @doi [ ] 10.1088/1538-3873/acbe66 , https://ui.adsabs.harvard.edu/abs/2023PASP..135d8003W 135, 048003

    Wright G. S., et al., 2023, @doi [ ] 10.1088/1538-3873/acbe66 , https://ui.adsabs.harvard.edu/abs/2023PASP..135d8003W 135, 048003

  101. [109]

    C., 2008a, @doi [arXiv e-prints] 10.48550/arXiv.0807.1272 , https://ui.adsabs.harvard.edu/abs/2008arXiv0807.1272W p

    Wyatt M. C., 2008a, @doi [arXiv e-prints] 10.48550/arXiv.0807.1272 , https://ui.adsabs.harvard.edu/abs/2008arXiv0807.1272W p. arXiv:0807.1272

  102. [110]

    C., 2008b, @doi [ ] 10.1146/annurev.astro.45.051806.110525 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..339W 46, 339

    Wyatt M. C., 2008b, @doi [ ] 10.1146/annurev.astro.45.051806.110525 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..339W 46, 339

  103. [111]

    C., Smith R., Su K

    Wyatt M. C., Smith R., Su K. Y. L., Rieke G. H., Greaves J. S., Beichman C. A., Bryden G., 2007, @doi [ ] 10.1086/518404 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..365W 663, 365

  104. [112]

    M., Rix H.-W., 2023, @doi [ ] 10.1093/mnras/stad1941 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z 524, 1855

    Zhang X., Green G. M., Rix H.-W., 2023, @doi [ ] 10.1093/mnras/stad1941 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z 524, 1855

  105. [113]

    Zuckerman B., 2019, @doi [ ] 10.3847/1538-4357/aaee66 , https://ui.adsabs.harvard.edu/abs/2019ApJ...870...27Z 870, 27

  106. [114]

    Zuckerman B., Song I., 2004, @doi [ ] 10.1146/annurev.astro.42.053102.134111 , https://ui.adsabs.harvard.edu/abs/2004ARA&A..42..685Z 42, 685

  107. [115]

    del Burgo C., Allende Prieto C., 2016, @doi [ ] 10.1093/mnras/stw2005 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.463.1400D 463, 1400

  108. [116]

    del Burgo C., Allende Prieto C., 2018, @doi [ ] 10.1093/mnras/sty1371 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.1953D 479, 1953

  109. [117]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.