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Fewer Companions in the Crowd: The Low Close Binary Fraction in Globular Clusters from Gaia RVS

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that globular clusters contain far fewer close binary stars than the field, with fractions of a few percent or less in the sampled outer regions of 10 clusters, and that this deficit shows no dependence on cluster…

desk verdict A useful homogeneous dataset of low close binary fractions in 10 GCs, but the headline 'significantly lower' claim is stronger than the statistics and selection corrections support. read the letter →

arxiv 2507.00131 v1 pith:2CWT5S62 submitted 2025-06-30 astro-ph.SR astro-ph.GA

Dolev Bashi , Vasily Belokurov This is my paper · ORCID
classification astro-ph.SRastro-ph.GA
keywords closebinariesglobularclustersGaiaRVSradialvelocityvariabilitybinaryfractionGaussianmixturemodelstellardynamicsmetallicity
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

Using Gaia's repeated radial-velocity measurements, this paper compares the close binary fraction in 10 globular clusters with that of field stars of similar stellar type. The authors find that only a few percent (or fewer) of the cluster stars they sample show radial-velocity variability indicative of short-period companions, well below the field at the same metallicities. They further find no significant dependence of the cluster binary fraction on metallicity, in contrast to a clear anti-correlation they recover in the field. If correct, the result means that dense stellar environments—or the later evolution of their stars—remove or merge close binaries far more effectively than the Galactic field does, and that metallicity plays a subordinate role in clusters.

What carries the argument

The load-bearing object is the Gaussian mixture model fitted to the distribution of the robust peak-to-peak radial-velocity semi-amplitude, $RV_{\rm pp}/2$, as a function of $G_{\rm RVS}$ magnitude. One Gaussian component describes single stars, whose mean follows $\mu_s(G) = a + \exp(b(G - G_0))$ and whose width $\sigma_s$ captures intrinsic jitter; the second, broader Gaussian describes binaries, with an extra variability scale $d$ and width $\sigma_b$, weighted by the binary fraction $F$. A Monte-Carlo forward model that mimics the Gaia DR3 cadence, noise, and selection then yields recovery factors $\varepsilon$ (1.18, 1.25, 1.45 for periods up to $10^2$, $10^3$, $10^4$ days) used to correct the raw fractions. The machinery converts an otherwise noisy and incomplete set of epoch radial velocities into a homogeneous, statistically comparable binary fraction for each cluster and for the field.

What would settle it

Take one cluster with an independent, high-precision binary census of the same bright giants (e.g., M4, with roughly 6000 VLT/FLAMES spectra). If a targeted analysis of those stars finds a substantial fraction, say more than 10%, of short-period binaries among stars that Gaia RVS classifies as single, the GMM's low fractions for that cluster would be ruled out. Alternatively, a Monte-Carlo injection test that adds a metallicity-dependent jitter term to the single-star Gaussian should change the recovered fractions materially; if it does, the reported values are not robust.

Watch

Extended reading notes

Core claim

The paper claims that the close binary fraction in the outer, resolved regions of 10 Galactic globular clusters is very low, generally a few percent or less, and significantly lower than the binary fraction of field stars of matched stellar parameters. Applying the same Gaussian-mixture decomposition of Gaia RVS peak-to-peak radial-velocity amplitudes to cluster members and field dwarfs and giants, the authors derive binary fractions that, after completeness correction, do not exceed about 10% for orbital periods up to $10^{4}$ days in any cluster. They also report that a linear fit of cluster binary fraction versus metallicity gives a slope of -0.021 ± 0.025, statistically consistent with zero, while the field giants and dwarfs show clearly negative slopes. The authors interpret the deficit as the combined action of dynamical disruption and hardening in dense environments and of common-envelope evolution that merges or ejects short-period companions during the giant phase.

Load-bearing premise

The model assumes that single stars of a given magnitude scatter around a single mean locus in log(RVpp/2) with one Gaussian width; if intrinsic single-star jitter is non-Gaussian or depends on metallicity—a possibility the paper itself raises when noting the higher jitter of metal-poor stars—the inferred binary fractions could be biased.

Editorial extensions

If this is right

  • The outer regions of globular clusters are strongly depleted in close binaries; surviving binaries are expected to be concentrated in the cores, where Gaia RVS cannot see them.
  • The absence of a metallicity trend implies that dynamical age and processing, not formation metallicity, set the present-day binary statistics of globular clusters.
  • The same RVS mixture method can be extended to dozens more clusters with modest numbers of members, giving a homogeneous census of globular-cluster binary fractions from one dataset.
  • The low giant binary fractions support the idea that many close binaries have already evolved through common-envelope phases into merged objects such as blue stragglers.

Reading between the lines

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

  • Extending the same analysis to open clusters, which are less dense, would test whether the field anti-correlation with metallicity re-emerges when dynamical processing weakens; the paper does not attempt this.
  • The elevated RV jitter the authors note in metal-poor stars, if astrophysical, could be a granulation signal; modelling it explicitly might lower the already-low binary fractions further and provide a new probe of convection in evolved metal-poor giants.
  • Future Gaia data releases with longer time baselines should detect longer-period binaries, so the completeness corrections (which assume 34 months) can be re-calibrated; the prediction is that recovered fractions will rise modestly but remain well below field values.
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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 / 5 minor

Summary. The paper estimates close binary fractions in 10 globular clusters and a field comparison sample using Gaia DR3 RVS peak-to-peak radial velocities. The analysis fits a Gaussian mixture model to the distribution of log(RVpp/2), decomposing it into single-star and binary components, and applies Monte Carlo completeness corrections to report fractions complete to orbital periods of 10^4 days. The authors report very low close binary fractions in the GCs, consistent with earlier studies, and claim that GCs possess a significantly lower close binary fraction than field stars, with no significant metallicity trend among the clusters.

Significance. If the results hold, this is a valuable homogeneous measurement of GC close binary fractions from Gaia RVS, extending the sample to an order of magnitude more clusters than previous spectroscopic surveys and providing a direct field-versus-GC comparison. The use of public Gaia and APOGEE data, a transparent MCMC fitting procedure, and explicit completeness corrections are strengths. However, the headline quantitative claims—especially 'significantly lower' and the absence of a metallicity trend—are not fully supported by the statistical evidence presented.

major comments (2)
  1. [Section 3, Table 2, Fig. 5] The abstract and Section 4 assert that GCs possess a 'significantly lower' close binary fraction than field stars, but no formal significance test is reported. The 16-84% posterior intervals in Table 2 are very broad for several clusters (e.g., NGC 3201: F = 1.85+13.20-1.50%, NGC 6397: 3.64+12.82-3.13%, M10: 2.23+10.48-1.85%), and these intervals overlap the field giant fractions at similar metallicities seen in Fig. 5. A quantitative comparison (e.g., posterior overlap probabilities, a hierarchical model, or a permutation test) is needed to support the 'significantly lower' claim.
  2. [Section 4, final paragraph; Appendix A] The mixture model in Appendix A models the single-star component as a Gaussian with mean mu_s(G) = a + exp(b(G-G0)) and a single width sigma_s, with no metallicity dependence. The final paragraph of Section 4 explicitly states that 'metal-poor stars exhibit noticeably higher RV jitter' and that an additional free parameter is needed to model this offset. Since the GC sample spans [Fe/H] from -0.72 to -2.17 and the highest inferred fractions in Table 2 are in the metal-poor clusters (NGC 4372, NGC 6397, NGC 3201), the reported flat metallicity slope (-0.021 +/- 0.025) and the low GC fractions could be biased by unmodeled jitter. A sensitivity analysis or a refit with a metallicity-dependent sigma_s (or a jitter offset) is required before the no-trend conclusion can be considered robust.
minor comments (5)
  1. [Section 2] The sentence 'we obtain 4, 897, 811 sources' contains awkward spacing in the number; use '4,897,811'.
  2. [Table 2 caption] The phrase 'following Appendix A completeness correction' is a fragment; rephrase as 'after applying the completeness correction described in Appendix A'.
  3. [Section 3] The sentence 'We restricted the sample to M* ≤ 1,M⊙' contains an errant comma; it should read 'M* ≤ 1 M⊙'.
  4. [Appendix A] The relation for mu_s(G) is presented in the text but is not numbered; adding an equation number would make it easier to reference.
  5. [Figure 1] The colored symbols for individual cluster members are difficult to distinguish from the grey-scale density background; larger markers or a higher-contrast colour scheme would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: binary fractions are fitted parameters and the completeness correction is an independent injection–recovery calibration; self-citations are supported by a self-contained appendix.

full rationale

The paper's central quantities are estimated, not predicted: the close binary fraction F is a free parameter in the Gaussian mixture model (Appendix A), fitted directly to Gaia RVS RVpp/2 data. There is no step in which an input is defined in terms of the output; the model's single-star locus mu_s(G) = a + exp(b(G-G0)) and widths are parameters with stated priors (Table A1), and the binary fraction is the fitted mixture weight. The completeness correction is an independent Monte-Carlo injection-recovery experiment: synthetic binaries with drawn orbital parameters are injected into mock RVS cadences, the full MCMC pipeline is re-run, and the recovery factor epsilon = F_est/F_true is applied. The injected F_true is varied; the target result (the real clusters' F) is not used as an input. The field-GC comparison is homogeneous because the identical estimator is applied to both samples, with the same choice to present uncorrected fractions in Fig. 5. Self-citations to Bashi et al. (2024) and Bashi & Tokovinin (2024) are not load-bearing: the method is fully restated in Appendix A, and the cited Bashi et al. (2023) completeness validation supplements, rather than substitutes for, the paper's own Monte Carlo. The admitted metallicity-dependent RV jitter in the final paragraph of Section 4 is an acknowledged limitation that could bias single-star jitter modeling and hence binary fractions, but this is a model-adequacy/correctness concern, not a circularity: it does not make the output equal to an input by construction. No circular step can be exhibited.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The model introduces seven fitted parameters per cluster; the binary fraction F is the central quantity. The method relies on Gaussian mixture assumptions and a synthetic completeness model. No new physical entities are postulated.

free parameters (7)
  • Binary fraction F (per cluster) = e.g., raw F=0.34% for 47 Tuc; corrected values in Table 2 (0.49+0.95-0.28%)
    Central quantity estimated by the Gaussian mixture model for each cluster.
  • Single-star mean offset a (per cluster) = fitted by MCMC, not tabulated
    Defines the magnitude-dependent locus of single-star RVpp/2.
  • Single-star mean slope b (per cluster) = fitted by MCMC, not tabulated
    Controls how the single-star locus varies with G_RVS.
  • Single-star magnitude pivot G0 (per cluster) = fitted by MCMC, not tabulated
    Pivot magnitude in the exponential form of the single-star mean.
  • Binary extra variability parameter d (per cluster) = fitted by MCMC, not tabulated
    Accounts for additional RV scatter in binary stars.
  • Single-star Gaussian width sigma_s (per cluster) = fitted by MCMC, not tabulated
    Width of the single-star component; the paper notes metal-poor stars may need an additional width parameter.
  • Binary Gaussian width sigma_b (per cluster) = fitted by MCMC, not tabulated
    Width of the binary component.
assumptions (5)
  • domain assumption The Gaia rv_amplitude_robust statistic is a reliable proxy for orbital RV variability after outlier removal.
    Section 2 uses RVpp as the working observable, assuming it reflects binarity rather than measurement artifacts.
  • ad hoc to paper The single-star RVpp/2 distribution is Gaussian with mean mu_s(G)=a+exp(b(G-G0)) and a single width sigma_s.
    Appendix A defines the mixture model; this shape is assumed, not derived, and the paper notes metal-poor stars show extra jitter.
  • domain assumption The synthetic binary population uses a log-uniform period distribution up to Pmax, uniform mass ratio q in [0.01,0.9], and Rayleigh eccentricity with scale 0.3 for P>5d.
    Appendix A Monte Carlo; the recovery factor epsilon depends on these assumed distributions.
  • domain assumption The completeness correction factor derived from synthetic stars applies to the real cluster populations.
    Appendix A applies epsilon=1.45 for Pmax<1e4 days uniformly to all clusters.
  • domain assumption Gaia RVS GC members, mostly in outer regions, are representative enough to compare with field stars.
    Section 2 and 4 acknowledge the radial bias, but the abstract makes a general claim without correcting for it.

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Cite this review

Pith. "Pith review of Fewer Companions in the Crowd: The Low Close Binary Fraction in Globular Clusters from Gaia RVS." pith.science (2026). https://pith.science/paper/2CWT5S62

@misc{pith2026250700131,
  author       = {Pith},
  title        = {Pith review of: Fewer Companions in the Crowd: The Low Close Binary Fraction in Globular Clusters from Gaia RVS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2CWT5S62}},
  note         = {Machine review of arXiv:2507.00131}
}
read the original abstract

In dense environments like globular clusters (GCs), dynamical interactions can disrupt or harden close binaries, nonetheless, detailed comparisons with field binary fractions remain limited. Here, we present an analysis of the close binary fraction in a carefully selected sample of field stars and 10 GCs using Gaia Radial Velocity Spectrometer (RVS) data, which is among the largest samples of GCs analysed using multi-epoch spectroscopy to date. By assessing the peak-to-peak variations of the sources' radial velocity (RV), we estimate the close binary fractions through a method that fits the distribution as the product of two Gaussian distributions. By applying the same RV-variability method to both cluster members and field stars, we ensure a homogeneous and inclusive comparison between the two environments. Despite matching stellar parameters between the field and GC samples, our findings confirm that GCs possess a significantly lower close binary fraction than field stars. Interestingly, we do not detect any clear trend of binary fraction with cluster metallicity; metal-rich and metal-poor GCs are uniformly binary-poor (within uncertainties). We discuss possible interpretations, including dynamical hardening in dense environments and the effects of common envelope evolution, which may lead to companion accretion or merger events.

Figures

Figures reproduced from arXiv: 2507.00131 by the authors.

Figure 1
Figure 1. CMDs of the 10 GCs analysed in this work. Grey-scale density maps show all high-probability members from the Vasiliev & Baumgardt (2021) catalogue, while coloured symbols highlight the subset with Gaia DR3 RVS information. The number of RVS stars in each cluster is given in parentheses. 2 4 6 8 rh [pc] 5 10 15 20 25 r R V [ p c ] 47Tuc Cen M4 NGC 6752 NGC 4372 NGC 3201 M13 NGC 6397 M10 NGC 4833 [PITH_FULL_IMAGE:fig… view at source ↗
Figure 2
Figure 2. Median RV scatter radius (𝑟RV) and MAD (y-error) for each globular cluster as a function of the cluster half-light radius (𝑟h). The red dashed line marks a 1:1 relative ratio. 𝑟RV = 𝑑 × √︁ (𝑥 2 + 𝑦 2) with 𝑥, 𝑦 are the projected angular positions of the sources relative to the cluster centre. We find the two properties consistent with each other, given the red dashed line marking a 1:1 ratio with one clear outlier, … view at source ↗
Figure 3
Figure 3. Mixture-model decomposition of Gaia-RVS variability for the 10 GCs. Each panel shows RVpp/2 versus 𝐺RVS for all cluster members. The solid red curve traces the posterior median locus of the single-star Gaussian component recovered by the Bayesian mixture fit. The inset in each panel prints the raw close binary fraction 𝐹 returned by the fit (median and 16–84 % interval; no completeness correction applied here). The … view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Effective temperature, 𝑇eff , as a function of the surface gravity, log 𝑔, of the Gaia-APOGEE field sample. The solid magenta and blue poly￾gons delineate our selection of dwarf and giant sources, respectively. Over￾laid are stellar GC members in our sample with availa…
Figure 5
Figure 5. Figure 5: Close binary fractions as a function of metallicity for field stars and globular cluster (GC) members. Field star samples, comprising dwarfs (magenta crosses) and giants (blue crosses), were selected by cross-matching the Gaia sample with the APOGEE catalogue, providin…

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Works this paper leans on

53 extracted references · 9 canonical work pages

  1. [1]

    Abdurro'uf 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]

    G., Belokurov V., Matsuno T., Montelius M., Monty S., Sanders J

    Ardern-Arentsen A., Kane S. G., Belokurov V., Matsuno T., Montelius M., Monty S., Sanders J. L., 2025, @doi [ ] 10.1093/mnras/staf096 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537.1984A 537, 1984

    work page doi:10.1093/mnras/staf096 2025

  3. [3]

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

    doi:10.3847/1538-4357/ac7c74 2022

  4. [4]

    D., 1995, @doi [ ] 10.1146/annurev.aa.33.090195.001025 , https://ui.adsabs.harvard.edu/abs/1995ARA&A..33..133B 33, 133

    Bailyn C. D., 1995, @doi [ ] 10.1146/annurev.aa.33.090195.001025 , https://ui.adsabs.harvard.edu/abs/1995ARA&A..33..133B 33, 133

    work page arXiv 1995

  5. [5]

    Bashi D., Tokovinin A., 2024, @doi [ ] 10.1051/0004-6361/202452637 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A.247B 692, A247

    doi:10.1051/0004-6361/202452637 2024

  6. [6]

    Bashi D., Mazeh T., Faigler S., 2023, @doi [ ] 10.1093/mnras/stad999 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.1184B 522, 1184

    doi:10.1093/mnras/stad999 2023

  7. [7]

    Bashi D., Belokurov V., Hodgkin S., 2024, @doi [ ] 10.1093/mnras/stae2385 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535..949B 535, 949

    doi:10.1093/mnras/stae2385 2024

  8. [8]

    Baumgardt H., Hilker M., 2018, @doi [ ] 10.1093/mnras/sty1057 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.1520B 478, 1520

    doi:10.1093/mnras/sty1057 2018

Show all 53 references
  1. [9]

    Belokurov V., Kravtsov A., 2022, @doi [ ] 10.1093/mnras/stac1267 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514..689B 514, 689

    2022 doi

  2. [10]

    Belokurov V., Kravtsov A., 2024, @doi [ ] 10.1093/mnras/stad3920 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.3198B 528, 3198

    2024 doi

  3. [11]

    Blomme R., et al., 2023, @doi [ ] 10.1051/0004-6361/202243685 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...7B 674, A7

    2023 doi

  4. [12]

    F., Nayak P

    Carrasco-Varela F. F., Nayak P. K., Puzia T. H., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.21051 , https://ui.adsabs.harvard.edu/abs/2025arXiv250321051C p. arXiv:2503.21051

    2025 doi arXiv

  5. [13]

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

    Choi J., Dotter A., Conroy C., Cantiello M., Paxton B., Johnson B. D., 2016, @doi [ ] 10.3847/0004-637X/823/2/102 , https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C 823, 102

    2016 doi

  6. [14]

    W., 1975, @doi [ ] 10.1086/181869 , https://ui.adsabs.harvard.edu/abs/1975ApJ...199L.143C 199, L143

    Clark G. W., 1975, @doi [ ] 10.1086/181869 , https://ui.adsabs.harvard.edu/abs/1975ApJ...199L.143C 199, L143

    1975 doi

  7. [15]

    Clementini G., et al., 2023, @doi [ ] 10.1051/0004-6361/202243964 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A..18C 674, A18

    2023 doi

  8. [16]

    R., Pasquato M., 2011, @doi [ ] 10.1088/0004-637X/743/1/11 , https://ui.adsabs.harvard.edu/abs/2011ApJ...743...11D 743, 11

    Dalessandro E., Lanzoni B., Beccari G., Sollima A., Ferraro F. R., Pasquato M., 2011, @doi [ ] 10.1088/0004-637X/743/1/11 , https://ui.adsabs.harvard.edu/abs/2011ApJ...743...11D 743, 11

    2011 doi

  9. [17]

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

    2016 doi

  10. [18]

    Foreman-Mackey D., et al., 2013, emcee: The MCMC Hammer , Astrophysics Source Code Library, record ascl:1303.002 ( @eprint ascl 1303.002 )

    2013

  11. [19]

    Gaia Collaboration et al., 2023a, @doi [ ] 10.1051/0004-6361/202243782 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A..34G 674, A34

    doi

  12. [20]

    Gaia Collaboration et al., 2023b, @doi [ ] 10.1051/0004-6361/202347203 , https://ui.adsabs.harvard.edu/abs/2023A&A...680A..35G 680, A35

    doi

  13. [21]

    C., Zhao H., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18320.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413.2509G 413, 2509

    Gieles M., Heggie D. C., Zhao H., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18320.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413.2509G 413, 2509

    2011

  14. [22]

    Giesers B., et al., 2019, @doi [ ] 10.1051/0004-6361/201936203 , https://ui.adsabs.harvard.edu/abs/2019A&A...632A...3G 632, A3

    2019 doi

  15. [23]

    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

  16. [24]

    R., Aarseth S

    Hurley J. R., Aarseth S. J., Shara M. M., 2007, @doi [ ] 10.1086/517879 , https://ui.adsabs.harvard.edu/abs/2007ApJ...665..707H 665, 707

    2007 doi

  17. [25]

    Hut P., et al., 1992, @doi [ ] 10.1086/133085 , https://ui.adsabs.harvard.edu/abs/1992PASP..104..981H 104, 981

    1992 doi

  18. [26]

    M., Rasio F

    Ivanova N., Belczynski K., Fregeau J. M., Rasio F. A., 2005, @doi [ ] 10.1111/j.1365-2966.2005.08804.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.358..572I 358, 572

    2005

  19. [27]

    O., Rasio F

    Ivanova N., Heinke C. O., Rasio F. A., Taam R. E., Belczynski K., Fregeau J., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10876.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372.1043I 372, 1043

    2006

  20. [28]

    Ivanova N., et al., 2013, @doi [ ] 10.1007/s00159-013-0059-2 , https://ui.adsabs.harvard.edu/abs/2013A&ARv..21...59I 21, 59

    2013 doi

  21. [29]

    N., 2015, @doi [ ] 10.1088/0004-637X/807/1/32 , https://ui.adsabs.harvard.edu/abs/2015ApJ...807...32J 807, 32

    Ji J., Bregman J. N., 2015, @doi [ ] 10.1088/0004-637X/807/1/32 , https://ui.adsabs.harvard.edu/abs/2015ApJ...807...32J 807, 32

    2015 doi

  22. [30]

    Kamann S., et al., 2020, @doi [ ] 10.1051/0004-6361/201936843 , https://ui.adsabs.harvard.edu/abs/2020A&A...635A..65K 635, A65

    2020 doi

  23. [31]

    Katz D., et al., 2023, @doi [ ] 10.1051/0004-6361/202244220 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...5K 674, A5

    2023 doi

  24. [32]

    Lucatello S., Sollima A., Gratton R., Vesperini E., D'Orazi V., Carretta E., Bragaglia A., 2015, @doi [ ] 10.1051/0004-6361/201526957 , https://ui.adsabs.harvard.edu/abs/2015A&A...584A..52L 584, A52

    2015 doi

  25. [33]

    P., et al., 2012, @doi [ ] 10.1051/0004-6361/201016384 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..16M 540, A16

    Milone A. P., et al., 2012, @doi [ ] 10.1051/0004-6361/201016384 , https://ui.adsabs.harvard.edu/abs/2012A&A...540A..16M 540, A16

    2012 doi

  26. [34]

    P., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.19214 , https://ui.adsabs.harvard.edu/abs/2025arXiv250319214M p

    Milone A. P., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.19214 , https://ui.adsabs.harvard.edu/abs/2025arXiv250319214M p. arXiv:2503.19214

    2025 doi arXiv

  27. [35]

    M., Badenes C., 2019, @doi [ ] 10.3847/1538-4357/ab0d88 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875...61M 875, 61

    Moe M., Kratter K. M., Badenes C., 2019, @doi [ ] 10.3847/1538-4357/ab0d88 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875...61M 875, 61

    2019 doi

  28. [36]

    Moni Bidin C., Catelan M., Altmann M., 2008, @doi [ ] 10.1051/0004-6361:20078782 , https://ui.adsabs.harvard.edu/abs/2008A&A...480L...1M 480, L1

    2008 doi

  29. [37]

    u ller-Horn J., G \

    M \"u ller-Horn J., G \"o ttgens F., Dreizler S., Kamann S., Martens S., Saracino S., Ye C. S., 2025, @doi [ ] 10.1051/0004-6361/202450709 , https://ui.adsabs.harvard.edu/abs/2025A&A...693A.161M 693, A161

    2025 doi

  30. [38]

    73, Structure and Evolution of Close Binary Systems

    Paczynski B., 1976, in Eggleton P., Mitton S., Whelan J., eds, IAU Symposium Vol. 73, Structure and Evolution of Close Binary Systems. p. 75

    1976

  31. [39]

    Paxton B., Bildsten L., Dotter A., Herwig F., Lesaffre P., Timmes F., 2011, @doi [ ] 10.1088/0067-0049/192/1/3 , https://ui.adsabs.harvard.edu/abs/2011ApJS..192....3P 192, 3

    2011 doi

  32. [40]

    M., et al., 2020, @doi [ ] 10.3847/1538-4357/ab8acc , https://ui.adsabs.harvard.edu/abs/2020ApJ...895....2P 895, 2

    Price-Whelan A. M., et al., 2020, @doi [ ] 10.3847/1538-4357/ab8acc , https://ui.adsabs.harvard.edu/abs/2020ApJ...895....2P 895, 2

    2020 doi

  33. [41]

    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

  34. [42]

    J., Pera M

    Rain M. J., Pera M. S., Perren G. I., Benvenuto O. G., Panei J. A., De Vito M. A., Carraro G., Villanova S., 2024, @doi [ ] 10.1051/0004-6361/202347499 , https://ui.adsabs.harvard.edu/abs/2024A&A...685A..33R 685, A33

    2024 doi

  35. [43]

    B., 2024, @doi [ ] 10.3847/1538-4357/ad4bdd , https://ui.adsabs.harvard.edu/abs/2024ApJ...968...80R 968, 80

    Rozner M., Perets H. B., 2024, @doi [ ] 10.3847/1538-4357/ad4bdd , https://ui.adsabs.harvard.edu/abs/2024ApJ...968...80R 968, 80

    2024 doi

  36. [44]

    R., Fusi Pecci F., 2008, @doi [ ] 10.1051/0004-6361:20079082 , https://ui.adsabs.harvard.edu/abs/2008A&A...481..701S 481, 701

    Sollima A., Lanzoni B., Beccari G., Ferraro F. R., Fusi Pecci F., 2008, @doi [ ] 10.1051/0004-6361:20079082 , https://ui.adsabs.harvard.edu/abs/2008A&A...481..701S 481, 701

    2008 doi

  37. [45]

    R., Heggie D

    Sommariva V., Piotto G., Rejkuba M., Bedin L. R., Heggie D. C., Mathieu R. D., Villanova S., 2009, @doi [ ] 10.1051/0004-6361:200810696 , https://ui.adsabs.harvard.edu/abs/2009A&A...493..947S 493, 947

    2009 doi

  38. [46]

    A., Imig J., Nitschelm C., Stassun K

    Stone-Martinez A., Holtzman J. A., Imig J., Nitschelm C., Stassun K. G., Brownstein J. R., 2024, @doi [ ] 10.3847/1538-3881/ad12a6 , https://ui.adsabs.harvard.edu/abs/2024AJ....167...73S 167, 73

    2024 doi

  39. [47]

    B., 2005, in Shopbell P., Britton M., Ebert R., eds, Astronomical Society of the Pacific Conference Series Vol

    Taylor M. B., 2005, in Shopbell P., Britton M., Ebert R., eds, Astronomical Society of the Pacific Conference Series Vol. 347, Astronomical Data Analysis Software and Systems XIV. p. 29

    2005

  40. [48]

    Tokovinin A., Moe M., 2020, @doi [ ] 10.1093/mnras/stz3299 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5158T 491, 5158

    2020 doi

  41. [49]

    Torres G., Andersen J., Gim \'e nez A., 2010, @doi [ ] 10.1007/s00159-009-0025-1 , https://ui.adsabs.harvard.edu/abs/2010A&ARv..18...67T 18, 67

    2010 doi

  42. [50]

    Vasiliev E., Baumgardt H., 2021, @doi [ ] 10.1093/mnras/stab1475 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.5978V 505, 5978

    2021 doi

  43. [51]

    arXiv:2502.17755

    Vernekar N., Lucatello S., Kuzma P., Spina L., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2502.17755 , https://ui.adsabs.harvard.edu/abs/2025arXiv250217755V p. arXiv:2502.17755

    2025 doi arXiv

  44. [52]

    Wragg F., et al., 2024, @doi [ ] 10.1093/mnras/stae2333 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535..781W 535, 781

    2024 doi

  45. [53]

    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 6, 2026 · model on record in the stance chip above.