Stellar masses and mass ratios for Gaia open cluster members

TL;DR

We introduce a fast Simulation-Based Inference framework to jointly infer individual stellar masses, mass ratios, and global cluster parameters for open clusters using Gaia DR3 parallaxes and multi-band photometry. The method employs an iterative, priors-informed SBI pipeline, including an empirical mass-ratio prior and a $q_{ m lim} = 0.1/M_A$ threshold, validated on simulated clusters and applied to 42 Gaia DR3 OCs to derive masses and mass ratios for 27{,}201 stars with typical uncertainties of $ obreak0.08$ in $q$ and $ obreak0.01\, m M_igodot$ in $M_A$. Results reveal a strong correlation of high-$q$ binary fractions with cluster age and a weak anti-correlation with metallicity, with mass-ratio distributions for $M_A ilde{>}\nobreak 0.6\,M_igodot$ resembling field-star trends. The approach enables population-level investigations of multiplicity and precision stellar masses across extended samples of open clusters, accounting for unresolved binaries in dynamical and evolutionary analyses.

Abstract

Context: Unresolved binaries in star clusters can bias stellar and cluster mass estimates, making their proper treatment essential for studying cluster dynamics and evolution. Aims: We aim to develop a fast and robust framework for jointly deriving stellar masses and multiplicity statistics of member stars, together with optimal cluster parameters. Methods: We use Gaia DR3 parallaxes together with multi-band photometry of open cluster (OC) members to infer stellar masses and binary mass-ratios through simulation-based inference (SBI), while iteratively fitting the cluster parameters. The validation of our SBI framework on simulated clusters demonstrates that the inclusion of infrared photometry significantly improves the detection of low mass-ratio binaries. The minimum mass-ratio threshold for reliably identifying unresolved binaries depends on cluster properties and the available photometry, but typically lies below $q=0.5$. Results: Applying our method to 42 well-populated OCs, we derive a catalogue of stellar masses and mass-ratios for 27201 stars, achieving typical uncertainties of 0.08 in $q$ and $0.01\,\mathrm{M}_\odot$ in the primary stellar mass. We analyse the archetype OCs M67 and NGC 2360 in detail, including mass segregation and mass-ratio distribution among other characteristics, while deriving multiplicity fractions for the rest of the sample. We find evidence that the high mass-ratio ($q\geq 0.6$) binary fraction shows a strong correlation with the age and a weak anti-correlation with the cluster metallicity. Furthermore, the variation of the binary fraction with stellar mass in OCs shows strong accordance with the observed dependence for field stars heavier than $\gtrsim0.6\,\mathrm{M}_\odot$. Conclusions: Our work paves a path for future population-level investigations of multiplicity statistics and precision stellar masses in extended samples of OCs.

Figures (26)

• Figure 1: A schematic diagram showing the workflow of obtaining the joint-posterior distribution of stellar mass and mass-ratio while iteratively updating the cluster parameters until convergence. We infer posteriors using SBI for all open cluster members (Sect. \ref{['subsec:model_param_sbi']}) and iteratively improve the estimated cluster parameters, log(Age), distance and $A_V$ (Sects. \ref{['subsec:apply_cluster_priors']} and \ref{['subsec:iter_cluster_param']}). We also make use of the empirical findings of binary fraction varying with stellar mass and incorporate it in a mass-ratio prior (Sect. \ref{['subsec:mass_ratio_prior']}).
• Figure 2: Corner plots of SBI posteriors for a simulated low-mass binary star ($M_{\mathrm{A}}\sim0.76\,\mathrm{M}_{\odot}, q\sim0.85, d=1$ kpc, $A_V\sim0.48$ mag, log(Age) [dex] $=9.5$, [Fe/H] [dex] = 0.0) with typical Gaia DR3 and 2MASS/WISE uncertainties for two cases: without (red) and with (blue) prior information about cluster parameters to obtain the weighted posterior distribution.
• Figure 3: Visualisation of the convergence of the iterative fitting loop (see Fig. \ref{['fig:iterative_schematic']}) for the case of M67 (NGC 2682). The left panel shows the PARSEC isochrone fits to the observed CMD for each iteration while the panels on the right illustrate the variation of cluster parameters across iterations before they converge in the first round at the 45th iteration.
• Figure 4: Recovered masses and mass-ratios of stars in a simulated solar-metallicity cluster ($d=1$ kpc, $A_V\sim0.1$ mag, log(Age) $=9.5$ dex). Modes of the unweighted (Top) and weighted (Bottom), using cluster parameter information, $M_\mathrm{A}$ and $q$ posteriors are shown w.r.t the simulated parameters. The colour of each data point represents the (fractional) uncertainty in the corresponding ($M_\mathrm{A}$) $q$, whereas black data points correspond to stars that are not in the MS or have $G\geq19$ mag. The dashed line in the bottom right plot indicates the $q_{\mathrm{thresh}}$ (see text for details), above which we can reliably predict the mass-ratio of an unresolved binary in this cluster.
• Figure 5: CMDs of NGC 2360 (top) and NGC 2682 (M67) (bottom). For each cluster, the CMDs are coloured by the primary masses and mass-ratios (left) and the corresponding uncertainties (right). The identified photometric outliers are shown by red crosses while the gray shaded region encloses the MS stars with $G \leq 19$ mag. The inferred cluster parameters, along with the spectroscopic metallicity used, are listed on top of each figure.