Related papers: Detecting wide binaries using machine learning alg…
We present a catalog of 99,203 wide binary systems, initially identified as common proper motion (CPM) pairs from a subset of ~5.2 million stars with proper motions $\mu > 40$ mas/year, selected from Gaia data release 2 (DR2) and the…
Wide binary stars are important for testing alternative models of gravitation in the weak-field regime and understanding the statistical outcomes of dynamical interactions in the general Galactic field. The Gaia mission's collection of…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…
We present a rigorous identification of candidates for dormant black holes (BHs) and neutron stars (NSs) in binaries using summary statistics from Gaia DR3, rather than full orbital solutions. Although Gaia astrometric orbits have already…
Featuring greatly improved astrometry and precise radial velocities (RVs), the second data release (DR2) from Gaia affords us a unique opportunity to test the validity of samples of stellar wide binaries. In a previous work, we presented a…
We examine the capacity to identify binary systems from astrometric errors and deviations alone. Until the release of the fourth Gaia data release we lack the full astrometric time series that the satellite records, but as we show can still…
Wide binaries, particularly in large numbers and as free from selection biases as possible, constitute a largely overlooked tool for studying the Galaxy. The goal of this review is to highlight the potential inherent to large samples of…
Since its launch in 2013, the Gaia space telescope has provided precise measurements of the positions and magnitudes of over 1 billion stars. This has enabled extensive searches for stellar and sub-stellar companions through astrometric and…
In preparation for the release of the astrometric orbits of Gaia, Shahaf et al. (2019) proposed a triage technique to identify astrometric binaries with compact companions based on their astrometric semi-major axis, parallax, and primary…
Preparing for the expected wealth of Gaia detections, we consider here a simple algorithm for classifying unresolved astrometric binaries with main-sequence (MS) primary into three classes: binaries with a probable MS secondary, with two…
We show how astrometric and spectroscopic errors introduced by an unresolved binary system can be combined to give estimates of the binary period and mass ratio. This can be performed analytically if we assume we see one or more full orbits…
In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of deliverables. This becomes ever more imminent as we enter the Gaia…
The occurrence of multiple stars, dominantly binaries, is studied using the \textit{Gaia}-ESA DR2 catalogue. We apply the optimized statistical method that we previously developed for the analysis of 2D patterns. The field of stars is…
Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a dataset…
Binary star systems are expected to follow Newtonian dynamics similarly to planetary systems. However, reports have been made of wide binary systems with separations around 0.01 pc and larger, showing potential deviations from standard…
Close binary interactions perform a key role in the formation and shaping of planetary nebulae. However only a small fraction of Galactic planetary nebulae are known to host close binary systems. Many such systems are detectable through…
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…
Identifying and removing binary stars from stellar samples is a crucial but complicated task. Regardless of how carefully a sample is selected, some binaries will remain and complicate interpretation of results, especially via flux…
The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a…
A detailed study of stellar populations in Milky Way (MW) satellite galaxies remains an observational challenge due to their faintness and fewer spectroscopically confirmed member stars. We use unsupervised machine learning methods to…