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REVIEW 3 major objections 6 minor 65 references

The Frequency and Mass-Ratio Distribution of Binaries in Clusters -- III: Probabilistic Generative Modelling of Six Young Open Clusters

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that the fraction of binary stars in young open clusters declines with age and that near-equal-mass pairs are lost fastest, a pattern it attributes to three-body dynamical processing of primordial close binaries.

desk verdict A careful but fragile claim: the FQ75-age decline is not robust until the authors' own noise-bias correction is propagated into the fits. read the letter →

arxiv 2411.16089 v1 pith:G73RBBEN submitted 2024-11-25 astro-ph.GA astro-ph.SR

Jason Alexander , Michael Albrow This is my paper · ORCID
classification astro-ph.GAastro-ph.SR PACS 98.20.Di97.80.-d
keywords binaries:generalopenclusters:mass-ratiodistributionbinaryfractioncolour-magnitudediagramsGaiaDR3probabilisticgenerativemodellingyoungclusters
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 tries to establish two linked trends in the binary-star content of young open clusters. Using a probabilistic generative model fitted to Gaia colour-magnitude diagrams of six clusters, it argues that the fraction of binaries (with mass ratio $q>0.5$) declines as clusters age, and that the fraction of those binaries with high mass ratios ($q>0.75$) declines with age as well, meaning near-equal-mass pairs are lost faster than other binaries. It introduces and applies the ratio $FQ_{75} = f_B(q>0.75)/f_B(q>0.5)$, which ranges from about 0.32 to 0.84 across the six clusters. If correct, the result implies that the observed mass-ratio distribution of a cluster is not set at birth but is reshaped over the first roughly 100 Myr by dynamical interactions, specifically by non-ionizing three-body encounters acting on a primordial population of close binaries with $q \simeq 1$.

What carries the argument

The machinery is a probabilistic generative model of the colour-magnitude diagram: each cluster is a mixture of single-star, binary-star, and outlier likelihoods, with single stars placed along a MIST isochrone and binaries treated as two stars on the same isochrone whose secondary-to-primary mass ratio $q$ is drawn from a parameterised distribution (Legendre-polynomial or histogram). The posterior is sampled with dynamic nested sampling and models are compared by their evidence. The specific device carrying the age trend is the ratio $FQ_{75} \equiv f_B(q>0.75)/f_B(q>0.5)$, chosen to be robust to the CMD degeneracy that hides binaries with $q\gtrsim0.75$. This statistic links the mass-ratio distribution to the binary fraction and to age; the paper checks its stability by shifting the isochrone colour and by injecting simulated binary populations with a known flat ratio.

What would settle it

Re-analyse the same six clusters (or a larger young-cluster sample) with ages determined from lithium depletion boundaries instead of adopted literature values, and recompute the FQ75-versus-age fit. If the youngest cluster that anchors the trend no longer sits at the high-FQ75 end, or if the fitted slope is within 1 sigma of zero, the claimed decline of high-mass-ratio binaries with age is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that two observable quantities—the binary-star frequency $f_B(q>0.5)$ and the high-mass-ratio metric $FQ_{75}$—both decrease with cluster age, while $FQ_{75}$ increases with the binary frequency. Collinder 69, the youngest cluster, has the highest binary fraction ($f_B(q\ge0.5)=0.272^{+0.034}_{-0.036}$) and an $FQ_{75}$ near 0.8, together with a sharp upturn in the mass-ratio distribution for $q\gtrsim0.9$; the older clusters ($\alpha$ Persei, Pleiades, NGC 6405, Trumpler 10, UPK 640) show flatter mass-ratio distributions and lower $FQ_{75}$. The paper interprets these trends as evidence that a primordial population of close binaries with $q\simeq1$ is progressively processed by non-ionizing three-body interactions: encounters that do not destroy the binary either eject the lowest-mass star and lower $q$, or remove angular momentum and hasten orbital decay and merger, while collisional ionization separately reduces the overall binary fraction.

Load-bearing premise

The cluster ages and metallicities used to build the isochrones are adopted from the literature and treated as exact, with no uncertainties propagated into the age-trend fits; if the true ages are different, especially for the youngest cluster that anchors the trend, the reported declines could weaken or invert.

Editorial extensions

If this is right

  • If the trends hold, the binary mass-ratio distribution of an open cluster is time-dependent: young clusters like Collinder 69 display an excess of $q\gtrsim0.9$ pairs that older clusters have lost.
  • $FQ_{75}$ becomes a practical, photometric age indicator for young clusters (roughly 10 to 100 Myr), since it declines with age in this sample.
  • Models of cluster evolution must include a primordial population of close, nearly equal-mass binaries, because collisional ionization alone cannot account for the preferential loss of high-$q$ binaries.
  • Comparisons with literature values for the same clusters should be made at matched $q$ thresholds and mass ranges, since the binary fraction varies substantially with both; the paper tabulates $q'$ from 0.4 through 0.9 to enable this.
  • The noise simulations imply that the noisiest clusters (Collinder 69, NGC 6405, UPK 640) may have $FQ_{75}$ biased high by roughly 0.15, so the true age decline could be steeper than the raw values suggest.

Reading between the lines

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

  • A testable extension of the paper's logic is that the initial mass-ratio distribution in star-forming regions may be even more strongly peaked at $q\simeq1$ than the youngest cluster here shows; embedded clusters or very young associations should show $FQ_{75}$ closer to 1.
  • Because the paper treats literature ages as exact, a homogeneous re-ageing of the six clusters, for example by lithium depletion boundaries, could either sharpen or erase the reported trends; the Figure 4 fits should be re-derived with age uncertainties folded in.
  • The positive $FQ_{75}$-versus-binary-fraction correlation suggests that binary richness and equal-mass preference are two aspects of one underlying population, so surveys that measure only $f_B(q>0.5)$ may miss the dynamical state of a cluster.
  • The non-ionizing explanation predicts a spatial signature: high-$q$ binaries should survive preferentially in cluster cores, while low-$q$ binaries populate the outer parts or tidal tails; Gaia proper motions could test this segregation directly.
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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 / 6 minor

Summary. The paper applies probabilistic generative modelling of Gaia DR3 colour-magnitude diagrams to six young open clusters (Collinder 69, alpha Persei, Pleiades, NGC 6405, Trumpler 10, UPK 640). For each cluster it infers the mass function, the binary mass-ratio distribution, and the binary fraction as a function of the mass-ratio threshold, using both Legendre-polynomial and histogram representations of P(q). The central results are (i) a decline of the binary fraction fB(q>0.5) with cluster age, (ii) an increase of the high-mass-ratio fraction FQ75 = fB(q>0.75)/fB(q>0.5) with the overall binary fraction, and (iii) a decline of FQ75 with age. The authors interpret the FQ75 trends as possible evidence for non-ionizing three-body processing of a primordial close-binary population. The modelling is carried out with nested sampling (DYNESTY), and the paper includes extensive appendices with posterior corner plots, model realisations, and sensitivity tests.

Significance. If the claimed trends are correct, the paper provides a coherent observational picture of how binary fractions and mass-ratio distributions evolve in young open clusters, with a concrete dynamical interpretation that can be tested by N-body simulations. The study has several genuine strengths: the generative model is applied carefully to six clusters with a documented likelihood and priors; the code is publicly available; the isochrone-position sensitivity test (Section 4.6) shows robustness to plausible colour shifts; and the noise simulations in Section 4.7 are a transparent, honest assessment of a systematic effect. The central weakness is that the quantified noise bias in FQ75 for the noisiest clusters is not propagated into the headline correlations, leaving the main claims vulnerable to the paper's own simulations.

major comments (3)
  1. [4.7 / Figure 4 / Table D1] The noise simulations in Section 4.7 show that for the noise levels of Collinder 69, NGC 6405, and UPK 640, the measured FQ75 is biased high by approximately 0.15, yet the linear fits reported in Table D1 and plotted in Figure 4 are computed from the uncorrected FQ75 values; the noise-corrected points are only shown for the Legendre row of Figure 4 and are not incorporated into the fits. These three clusters include the two youngest clusters, and Section 4.5 states that the positive fB-FQ75 correlation is heavily dependent on the position of Collinder 69. Correcting these points downward by ~0.15 could therefore flatten or even reverse the FQ75-age slope and substantially weaken the FQ75-versus-fB correlation, directly threatening claim (ii) in Section 5. The authors should provide corrected FQ75 values for all affected clusters, recompute the orthogonal-distance-regression fits with the bias propagated (either by subtracting the simulation-derived bias or by jointly fitting a bias term), and report whether the slopes remain nonzero. In addition, the bias should be tested for the non-flat mass-ratio distributions actually inferred for these clusters, since the simulations assume a flat P(q).
  2. [3.1 / Table 2 / Figure 4] The cluster ages and metallicities in Table 2 are adopted from the literature and treated as fixed inputs when constructing the isochrones, and the age-trend fits in Figure 4 use these adopted ages without assigning them uncertainties. The cited age ranges are wide (e.g., Collinder 69 at 5-16 Myr, UPK 640 at 25-35 Myr, Pleiades at 75-150 Myr), so the slopes in Table D1 for age versus fB and age versus FQ75 may not be robust to plausible changes in the adopted ages, particularly for the youngest clusters that anchor the age trends. The authors should either adopt a consistent set of ages with uncertainties and propagate them into the orthogonal distance regression, or demonstrate that the claimed declines of binary fraction and FQ75 with age persist over the allowed age ranges of the youngest clusters.
  3. [4.7 / Table 3] The numbers of stars listed for the observed clusters in Section 4.7 (Col 69: 529; alpha Per: 176; Pleiades: 275; NGC 6405: 392; Trumpler 10: 267; UPK 640: 276) do not match the sample sizes in Table 3 (297, 157, 323, 378, 290, 145). This discrepancy matters because the comparison of the simulations (N=200 vs N=1000) to the observed clusters is used to estimate the expected scatter and the magnitude of the FQ75 bias; the authors should clarify which sample definition is used in each place and reconcile the numbers or explain the difference.
minor comments (6)
  1. [Table 3] In the Pleiades histogram (10 bins) row, the lower uncertainty on FQ75 is printed as `-0.87`; this should presumably be `-0.087`.
  2. [4.7] The text says `greatly effect the trends` and `nosiest clusters`; these should be `greatly affect the trends` and `noisiest clusters`.
  3. [References / Section 5] Duquennoy & Mayor (1991a) and (1991b) appear to refer to the same paper (A&A 248, 485) and should be merged into a single reference.
  4. [Figure A2 caption] The caption of Figure A2 says `Legendre mass-ratio distribution representation`, but the figure appears to show histogram realisations; the caption should be corrected or clarified.
  5. [3.4] The phrase `gamma distribution distribution` contains a duplicated word.
  6. [References] The in-text citation `Moe & Stefano (2017)` should be `Moe & Di Stefano (2017)` to match the reference list.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central trends are measured correlations from independent Gaia DR3 data; self-citations are present but not load-bearing.

full rationale

The paper's central claims are correlations among quantities (f_B(q>0.5), FQ75, adopted cluster age) that are derived from fitting a generative model to six previously unmodeled clusters with independent Gaia DR3 photometry. The model itself (Albrow & Ulusele 2022; Albrow 2024) is self-cited, but its functional forms are stated openly and its parameters are free; nothing in the likelihood or priors forces FQ75 to increase with f_B or to decline with age, as shown by the non-monotonic values in Table 3 (e.g., alpha Persei has higher f_B than the Pleiades but lower FQ75). FQ75 is defined as a ratio of two integrals of the fitted mass-ratio distribution, and the reported relations are measured posterior correlations, not identities. The adopted cluster ages are external literature values, and the three-body processing explanation is presented as a hypothesis rather than derived from the fit. The self-references to the earlier model and to the FQ75 metric, and the statement that the f_B-FQ75 correlation is consistent with Albrow (2024), are corroborative rather than load-bearing, especially since external comparisons (Pang et al. 2023; Jadhav et al. 2021; Donada et al. 2023) are provided. Section 4.7 identifies a real noise-induced upward bias in FQ75 for the noisiest clusters and plots one noise-corrected value, but the final fits use the uncorrected values; this is a statistical robustness concern and does not make the derivation circular. No step in the paper reduces by construction to its own inputs, so the circularity score is low; the minor self-citation warrants 2 rather than 0.

Assumptions & free parameters 17 free parameters · 6 assumptions · 0 invented entities

The central measurement of binary fractions and mass-ratio distributions relies on a generative model with 16-20 fitted parameters per cluster, on fixed external isochrone parameters, and on hand-chosen membership cuts. The free parameters are the usual nuisance and shape parameters of the model; the key derived quantities FQ75 and fB(q>0.5) are integrals over the fitted mass-ratio distribution.

free parameters (17)
  • gamma = 1.10 to 5.16 across clusters
    Power-law slope of the stellar mass function, fitted per cluster (Table B1).
  • c0 = e.g. 5.06 (Collinder 69); frozen for others
    Coefficient of linear term in mass function, fitted where evidence favored it (Table B1).
  • c1 = e.g. -3.28 (Collinder 69); frozen for others
    Slope of linear term in mass function, fitted where evidence favored it.
  • k = log10 k = 1.77 (Collinder 69); 1.76 (UPK 640)
    tanh cutoff sharpness for low-mass detection sensitivity, fitted for two clusters.
  • M0 = 0.13 (Collinder 69); 0.10 (UPK 640)
    tanh cutoff location for low-mass detection sensitivity, fitted for two clusters.
  • log h = 0.73 to 0.97 across clusters
    Error bar scaling factor applied to photometric uncertainties.
  • h_dot = frozen at 0 for all clusters
    Slope of error scaling with magnitude; evidence did not favor freeing it.
  • fB0 = 0.65 to 0.94 across clusters
    Binary fraction parameter at the reference mass, fitted per cluster.
  • fB_dot = e.g. -0.06 (Collinder 69); frozen for others
    Slope of binary fraction along the main sequence, fitted where evidence favored it.
  • fO = 0.02 to 0.06 across clusters
    Outlier fraction in the mixture model.
  • sigma = 1.02 to 1.29 across clusters
    Hyperparameter controlling prior width of Legendre mass-ratio coefficients.
  • a1 to a5 = ranges in Table B1, e.g. a2=1.06 to 2.43
    Legendre polynomial coefficients shaping the mass-ratio distribution.
  • histogram mass-ratio bin values (10 bins) = not tabulated in Table B1
    Non-parametric representation of the mass-ratio distribution with Dirichlet priors.
  • proper-motion selection radius = 2, 10, 20, 2, 2, 10 mas/yr
    Radius of the hard proper-motion membership cut, chosen by hand (Table 1).
  • parallax uncertainty cutoff = 30%
    Hand-chosen threshold for excluding poor parallax measurements.
  • 3D Gaussian membership probability threshold = 0.7
    Hand-chosen threshold for rejecting non-members in proper motion-parallax space.
  • DBSCAN epsilon and minPts = epsilon=2.0, minPts=15
    Hand-chosen DBSCAN parameters used as a membership check.
assumptions (6)
  • domain assumption Single stars lie exactly on a single MIST isochrone of fixed age and metallicity; all scatter comes from photometric uncertainty scaled by h.
    Section 3.1 and 3.2 define the likelihood around a single isochrone.
  • domain assumption For binaries, the primary is drawn from the same mass distribution as single stars, and the mass ratio q is independent of primary mass.
    Section 3.2 states this factorization, which is required to map CMD positions to mass ratios.
  • domain assumption The mass-ratio distribution is only constrained for q >= 0.4; q < 0.4 is excluded as indistinguishable from single stars.
    Section 4.2 normalizes distributions over 0.4 <= q <= 1.0, and fB(q>0.5) is obtained by integrating the fitted distribution.
  • domain assumption The adopted cluster ages and metallicities from literature references in Table 2 are correct.
    Section 3.1 uses these fixed values for isochrones; Figure 4 age trends depend on them.
  • domain assumption The MIST isochrone luminosity correction for 0.25-0.85 solar mass stars (Brandner et al. 2023) is applied correctly.
    Section 3.1 adjusts M_G to match the main-sequence ridge line.
  • domain assumption Membership selection cuts recover an unbiased cluster sample without completeness correction.
    Section 2.1-2.2 uses hand-chosen cuts; Section 2.1 acknowledges proper-motion trails mean selection may not include all members.

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Cite this review

Pith. "Pith review of The Frequency and Mass-Ratio Distribution of Binaries in Clusters -- III: Probabilistic Generative Modelling of Six Young Open Clusters." pith.science (2026). https://pith.science/paper/G73RBBEN

@misc{pith2026241116089,
  author       = {Pith},
  title        = {Pith review of: The Frequency and Mass-Ratio Distribution of Binaries in Clusters -- III: Probabilistic Generative Modelling of Six Young Open Clusters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G73RBBEN}},
  note         = {Machine review of arXiv:2411.16089}
}
abstract

We apply probabilistic generative modelling of colour-magnitude diagrams to six young Galactic open star clusters and determine their mass functions, binary mass-ratio distributions, and the frequencies of binary stars. We find that younger clusters tend to exhibit a higher incidence of binaries than their older counterparts. The mass-ratio distribution is fairly flat for the clusters with one exception that exhibits a sharp increase for $q\gtrsim0.9$. The ratio of the number of cluster binaries for which $q>0.75$ to the number of binaries for which $q>0.5$ (referred to as $FQ_{75}$) ranges from $\sim0.4 - 0.8$. This metric increases with the binary-star frequency of a cluster, but declines with cluster age. This may be due to non-ionizing 3-body dynamical processing of a primordial population of close binaries with initial mass ratios, $q \simeq 1$.

Figures

Figures reproduced from arXiv: 2411.16089 by the authors.

Figure 1
Figure 1. Proper motions of all stars within RA and declination selection around the centre of Pleiades. The black points are the background stars and the blue points are the stars included in our initial selection of possible cluster members. et al. (2023) revealed the median multiplicity fraction of 202 open clusters to be 18% for 𝑓𝐵(𝑞 > 0.6). Binary systems are significantly rarer in globular clusters. Milone et al. (2012)… view at source ↗
Figure 2
Figure 2. Mass functions for each cluster. Blue lines are based on 1000 random samples from the posterior distribution. The orange line shows the maximum probability solution. The Salpeter (1955) and Chabrier (2003) initial mass functions are shown as green and black dashed lines respectively [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The mass-ratio distribution using Legendre polynomials and histograms with ten bins for each cluster. The blue lines are 1000 random samples from the posterior distribution, and the red line indicates the maximum probability solution. 4.1 Mass Functions [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Inter-relations between 𝑓𝐵, 𝐹𝑄75 and cluster age for different mass-ratio distribution function representations. MNRAS 000, 1–11 (2024) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Top left panel: CMD of Pleiades. The green dashed-line indicates the isochrone used in the analysis. A range of tested isochrone shifts in 𝐺 − 𝑅𝑃 is indicated with the magenta dashed-lines. Remaining panels show the sensitivity of 𝑓𝐵 (𝑞 > 0.5), 𝐹𝑄75, and data error sca…
Figure 6
Figure 6. Figure 6: 𝐹𝑄75 vs 𝑓𝐵 (𝑞 > 0.5) from simulated CMDs with 200 or 1000 stars and a flat binary mass-ratio distribution. Simulated stars were given data uncertainties and gaussian scatter the same as Pleiades (for a given 𝐺). Columns are from analyses with mass-ratio distribution fu…

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Works this paper leans on

65 extracted references · 22 canonical work pages

  1. [1]

    Abdurro'uf Accetta K., Aerts C., et al., 2022, @doi [ ] 10.3847/1538-4365/ac4414 , https://ui.adsabs.harvard.edu/abs/2022ApJS..259...35A 259, 35

    doi:10.3847/1538-4365/ac4414 2022

  2. [2]

    Ahumada J., Lapasset E., 1995, , https://ui.adsabs.harvard.edu/abs/1995A&AS..109..375A 109, 375

    work page 1995

  3. [3]

    D., 2024, @doi [ ] 10.1093/mnras/stae425 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.6211A 528, 6211

    Albrow M. D., 2024, @doi [ ] 10.1093/mnras/stae425 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.6211A 528, 6211

    doi:10.1093/mnras/stae425 2024

  4. [4]

    D., Ulusele I

    Albrow M. D., Ulusele I. H., 2022, @doi [ ] 10.1093/mnras/stac1731 , 515, 730

    work page doi:10.1093/mnras/stac1731 2022

  5. [5]

    D., Gilliland R

    Albrow M. D., Gilliland R. L., Brown T. M., Edmonds P. D., Guhathakurta P., Sarajedini A., 2001, @doi [ ] 10.1086/322353 , 559, 1060

    doi:10.1086/322353 2001

  6. [6]

    H., Terndrup D

    Andronov N., Pinsonneault M. H., Terndrup D. M., 2006, @doi [ ] 10.1086/505127 , https://ui.adsabs.harvard.edu/abs/2006ApJ...646.1160A 646, 1160

    doi:10.1086/505127 2006

  7. [7]

    L., 1999, @doi [ ] 10.1086/306564 , 510, 266

    Basri G., Martín E. L., 1999, @doi [ ] 10.1086/306564 , 510, 266

    work page doi:10.1086/306564 1999

  8. [8]

    B., Solano E., Guti \' e rrez R., Morales-Calder \' o n M., Allard F., 2008, @doi [ ] 10.1051/0004-6361:200810395 , 492, 277

    Bayo A., Rodrigo C., y Navascu \' e s D. B., Solano E., Guti \' e rrez R., Morales-Calder \' o n M., Allard F., 2008, @doi [ ] 10.1051/0004-6361:200810395 , 492, 277

    doi:10.1051/0004-6361:200810395 2008

Show all 65 references
  1. [9]

    R., Vesperini E., 2023, @doi [ ] 10.3847/1538-4357/acb434 , 945, 164

    Bhat B., Lanzoni B., Ferraro F. R., Vesperini E., 2023, @doi [ ] 10.3847/1538-4357/acb434 , 945, 164

    2023 doi

  2. [10]

    Brandner W., Calissendorff P., Kopytova T., 2023, @doi [ ] 10.1093/mnras/stac2247 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518..662B 518, 662

    2023 doi

  3. [11]

    Cantat-Gaudin T., et al., 2020, @doi [ ] 10.1051/0004-6361/202038192 , https://ui.adsabs.harvard.edu/abs/2020A&A...640A...1C 640, A1

    2020 doi

  4. [12]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , 115, 763

    2003 doi

  5. [13]

    Choi J., et al., 2016, @doi [ ] 10.3847/0004-637X/823/2/102 , https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C 823, 102

    2016 doi

  6. [14]

    S., Reipurth B., Tokunaga A

    Connelley M. S., Reipurth B., Tokunaga A. T., 2008, @doi [ ] 10.1088/0004-6256/135/6/2526 , https://ui.adsabs.harvard.edu/abs/2008AJ....135.2526C 135, 2526

    2008 doi

  7. [15]

    Cordoni G., et al., 2023, @doi [ ] 10.1051/0004-6361/202245457 , https://ui.adsabs.harvard.edu/abs/2023A&A...672A..29C 672, A29

    2023 doi

  8. [16]

    E., 2015, @doi [ ] 10.1088/0004-637X/813/2/108 , https://ui.adsabs.harvard.edu/abs/2015ApJ...813..108D 813, 108

    Dahm S. E., 2015, @doi [ ] 10.1088/0004-637X/813/2/108 , https://ui.adsabs.harvard.edu/abs/2015ApJ...813..108D 813, 108

    2015 doi

  9. [17]

    J., Mathieu R

    Dolan C. J., Mathieu R. D., 1999, @doi [ ] 10.1086/301075 , 118, 2409

    1999 doi

  10. [18]

    Donada J., 2022, Master's thesis, Universitat de Barcelona

    2022

  11. [19]

    Donada J., et al., 2023, @doi [ ] 10.1051/0004-6361/202245219 , 675, A89

    2023 doi

  12. [20]

    Dotter A., 2016, @doi [ ] 10.3847/0067-0049/222/1/8 , https://ui.adsabs.harvard.edu/abs/2016ApJS..222....8D 222, 8

    2016 doi

  13. [21]

    Duquennoy A., Mayor M., 1991a, , https://ui.adsabs.harvard.edu/abs/1991A&A...248..485D 248, 485

  14. [22]

    Duquennoy A., Mayor M., 1991b, , https://ui.adsabs.harvard.edu/abs/1991A&A...248..485D 248, 485

  15. [23]

    H., Postnikova E

    Elsanhoury W. H., Postnikova E. S., Chupina N. V., et al., 2018, @doi [ ] 101007/s10509-018-3268-3 , http://dx.doi.org/10.1007/s10509-018-3268-3 363, 58

    2018 doi

  16. [25]

    Fürnkranz V., Rix H.-W., Coronado J., Seeburger R., 2023, @doi [ ] 10.48550/arXiv.2311.10133

    2023 doi arXiv

  17. [26]

    F., 1985, in Eggleton P

    Griffin R. F., 1985, in Eggleton P. P., Pringle J. E., eds, NATO Advanced Study Institute (ASI) Series C Vol. 150, Interacting Binaries. p. 1, @doi 10.1007/978-94-009-5337-6_1

    1985 doi

  18. [27]

    F., McCullough P

    Healy B. F., McCullough P. R., Schlaufman K. C., Kovacs G., 2023, @doi [ ] 10.3847/1538-4357/acad7b , 944, 39

    2023 doi

  19. [28]

    C., 1975, @doi [ ] 10.1093/mnras/173.3.729 , https://ui.adsabs.harvard.edu/abs/1975MNRAS.173..729H 173, 729

    Heggie D. C., 1975, @doi [ ] 10.1093/mnras/173.3.729 , https://ui.adsabs.harvard.edu/abs/1975MNRAS.173..729H 173, 729

    1975 doi

  20. [29]

    Higson E., Handley W., Hobson M., Lasenby A., 2019, @doi [Statistics and Computing] 10.1007/s11222-018-9844-0 , https://ui.adsabs.harvard.edu/abs/2019S&C....29..891H 29, 891

    2019 doi

  21. [30]

    G., 1975, @doi [ ] 10.1086/111815 , https://ui.adsabs.harvard.edu/abs/1975AJ.....80..809H 80, 809

    Hills J. G., 1975, @doi [ ] 10.1086/111815 , https://ui.adsabs.harvard.edu/abs/1975AJ.....80..809H 80, 809

    1975 doi

  22. [31]

    Hurley J., Pols O., Aarseth S., Tout C., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09448.x , 363

    2005

  23. [32]

    V., Roy K., Joshi N., Subramaniam A., 2021, @doi [ ] 10.3847/1538-3881/ac2571 , 162, 264

    Jadhav V. V., Roy K., Joshi N., Subramaniam A., 2021, @doi [ ] 10.3847/1538-3881/ac2571 , 162, 264

    2021 doi

  24. [33]

    S., Walter F

    Kim J. S., Walter F. M., Wolk S. J., 2005, @doi [ ] 10.1086/428002 , 129, 1564

    2005 doi

  25. [34]

    C., 2001, Ph D thesis, University of Johannesburg

    Koen M. C., 2001, Ph D thesis, University of Johannesburg

    2001

  26. [35]

    A., Gilmore G., 1990, , https://ui.adsabs.harvard.edu/abs/1990MNRAS.244...76K 244, 76

    Kroupa P., Tout C. A., Gilmore G., 1990, , https://ui.adsabs.harvard.edu/abs/1990MNRAS.244...76K 244, 76

    1990

  27. [36]

    Li L., Shao Z., Li Z.-Z., Yu J., Zhong J., Chen L., 2020, @doi [ ] 10.3847/1538-4357/abaef3 , https://ui.adsabs.harvard.edu/abs/2020ApJ...901...49L 901, 49

    2020 doi

  28. [37]

    Li J., et al., 2022, @doi [ ] 10.3847/1538-4357/ac731d , 933, 119

    2022 doi

  29. [38]

    Astr, https://ui.adsabs.harvard.edu/abs/1968ArA.....5....1L 5, 1

    Lindoff U., 1968, Ark. Astr, https://ui.adsabs.harvard.edu/abs/1968ArA.....5....1L 5, 1

    1968

  30. [39]

    V., 2006, @doi [ ] 10.1086/503900 , 131, 2967

    Makarov V. V., 2006, @doi [ ] 10.1086/503900 , 131, 2967

    2006 doi

  31. [40]

    A., Mikhnevich V

    Malofeeva A. A., Mikhnevich V. O., Carraro G., Seleznev A. F., 2023, @doi [ ] 10.3847/1538-3881/aca666 , 165, 45

    2023 doi

  32. [41]

    C., 1981, , https://ui.adsabs.harvard.edu/abs/1981A&A....97..235M 97, 235

    Mermilliod J. C., 1981, , https://ui.adsabs.harvard.edu/abs/1981A&A....97..235M 97, 235

    1981

  33. [42]

    Mermilliod J.-C., Queloz D., Mayor M., 2008, @doi [ ] https://doi.org/10.1051/0004-6361:200810016 , 488, 409

    2008 doi

  34. [43]

    P., et al., 2012, @doi [ ] 10.1051/0004-6361/201016384 , 540, A16

    Milone A. P., et al., 2012, @doi [ ] 10.1051/0004-6361/201016384 , 540, A16

    2012 doi

  35. [44]

    D., 2017, @doi [ ] 10.3847/1538-4365/aa6fb6 , 230, 15

    Moe M., Stefano R. D., 2017, @doi [ ] 10.3847/1538-4365/aa6fb6 , 230, 15

    2017 doi

  36. [45]

    V., Kulesh M

    Nikiforova V. V., Kulesh M. V., Seleznev A. F., Carraro G., 2020, @doi [ ] 10.3847/1538-3881/aba753 , 160, 142

    2020 doi

  37. [46]

    Niu H., Wang J., Fu J., 2020, @doi [ ] 10.3847/1538-4357/abb8d6 , https://ui.adsabs.harvard.edu/abs/2020ApJ...903...93N 903, 93

    2020 doi

  38. [47]

    Mirouh, G

    Pamos Ortega, D. Mirouh, G. M. Garc\' a Hern\'andez, A. Su\'arez Yanes, J. C. Barcel\'o Forteza, S. 2023, @doi [ ] 10.1051/0004-6361/202346323 , 675, A167

    2023 doi

  39. [48]

    Pang X., et al., 2022, @doi [ ] 10.3847/1538-4357/ac674e , 931, 156

    2022 doi

  40. [49]

    Pang X., et al., 2023, @doi [ ] 10.3847/1538-3881/ace76c , 166, 110

    2023 doi

  41. [50]

    K., Maitzen H., Claret A., Pintado O., 2006, @doi [ ] 10.1051/0004-6361:20054628 , 454, 171

    Paunzen E., Netopil M., Iliev I. K., Maitzen H., Claret A., Pintado O., 2006, @doi [ ] 10.1051/0004-6361:20054628 , 454, 171

    2006 doi

  42. [51]

    Paxton B., et al., 2018, @doi [ ] 10.3847/1538-4365/aaa5a8 , https://ui.adsabs.harvard.edu/abs/2018ApJS..234...34P 234, 34

    2018 doi

  43. [52]

    J., Dobbie P

    Pinfield D. J., Dobbie P. D., Jameson R. F., Steele I. A., Jones H. R. A., Katsiyannis A. C., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06630.x , 342, 1241

    2003

  44. [53]

    Raghavan D., et al., 2010, @doi [ ] 10.1088/0067-0049/190/1/1 , https://ui.adsabs.harvard.edu/abs/2010ApJS..190....1R 190, 1

    2010 doi

  45. [54]

    M., Connelley M

    Reipurth B., Guimar \ a es M. M., Connelley M. S., Bally J., 2007, @doi [ ] 10.1086/523596 , https://ui.adsabs.harvard.edu/abs/2007AJ....134.2272R 134, 2272

    2007 doi

  46. [55]

    E., 1955, @doi [ ] 10.1086/145971 , https://ui.adsabs.harvard.edu/abs/1955ApJ...121..161S 121, 161

    Salpeter E. E., 1955, @doi [ ] 10.1086/145971 , https://ui.adsabs.harvard.edu/abs/1955ApJ...121..161S 121, 161

    1955 doi

  47. [56]

    H., Ann H

    Sim G., Lee S. H., Ann H. B., Kim S., 2019, @doi [J. Korean Astron. Soc.] 10.5303/JKAS.2019.52.5.145 , 52, 145

    2019 doi

  48. [57]

    R., Laskar T., Valenti J

    Soderblom D. R., Laskar T., Valenti J. A., Stauffer J. R., Rebull L. M., 2009, @doi [ ] 10.1088/0004-6256/138/5/1292 , 138, 1292

    2009 doi

  49. [58]

    Sollima A., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13387.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.388..307S 388, 307

    2008

  50. [59]

    A., Beccari G., Ferraro F

    Sollima A., Carballo-Bello J. A., Beccari G., Ferraro F. R., Pecci F. F., Lanzoni B., 2010, @doi [ ] 10.1111/j.1365-2966.2009.15676.x , 401, 577

    2010

  51. [60]

    S., 2020, @doi [ ] 10.1093/mnras/staa278 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S 493, 3132

    Speagle J. S., 2020, @doi [ ] 10.1093/mnras/staa278 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S 493, 3132

    2020 doi

  52. [61]

    Stepien K., 1995, @doi [ ] 10.1093/mnras/274.4.1019 , https://ui.adsabs.harvard.edu/abs/1995MNRAS.274.1019S 274, 1019

    1995 doi

  53. [62]

    A., 1991, @doi [ ] 10.1093/mnras/250.4.701 , https://ui.adsabs.harvard.edu/abs/1991MNRAS.250..701T 250, 701

    Tout C. A., 1991, @doi [ ] 10.1093/mnras/250.4.701 , https://ui.adsabs.harvard.edu/abs/1991MNRAS.250..701T 250, 701

    1991 doi

  54. [63]

    Vleeming G., 1974, , https://ui.adsabs.harvard.edu/abs/1974A&AS...16..331V 16, 331

    1974

  55. [64]

    Yalyalieva L., Carraro G., Glushkova E., Munari U., Ochner P., 2022, @doi [ ] 10.1093/mnras/stac1199 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513.5299Y 513, 5299

    2022 doi

  56. [65]

    Yalyalieva L., Chemel A., Carraro G., Glushkova E., 2024, @doi [ ] 10.3847/1538-3881/ad1ff0 , https://ui.adsabs.harvard.edu/abs/2024AJ....167..100Y 167, 100

    2024 doi

  57. [66]

    P., 1977, , https://ui.adsabs.harvard.edu/abs/1977A&A....57..383Z 57, 383

    Zahn J. P., 1977, , https://ui.adsabs.harvard.edu/abs/1977A&A....57..383Z 57, 383

    1977

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.