Related papers: Unsupervised learning for variability detection wi…

Understanding which sources are present in an astronomical catalogue and which are not is crucial for the accurate interpretation of astronomical data. In particular, for the multidimensional Gaia data, filters and cuts on different…

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…

Gaia DR3 mission has identified and provided about 440,000 binary systems with orbital solutions, offering a valuable resource for searching binaries including a compact component. By combining the Gaia DR3 data with radial velocities (RVs)…

The separation of oxygen- and carbon-rich AGB sources is crucial for their accurate use as local and cosmological distance and age/metallicity indicators. We investigate the use of unsupervised learning algorithms for classifying the…

Context. The observed scarcity of brown dwarfs in close orbits (within 10 au) around solar-type stars poses significant questions about the origins of these substellar companions. These questions impact our broader understanding of…

There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…

Gaia DR3 provides >181, 000 radial velocity solutions for single-lined spectroscopic binaries (SB1s) which can be used to search for non-interacting compact object+star binary candidates by selecting systems with large mass functions. We…

Context. Planetary nebulae (PNe) are a brief but important phase of stellar evolution. The study of Galactic PNe has historically been hampered by uncertain distances, but the parallaxes of PN central stars (CSPNe) measured by Gaia are…

The present work aims to build a new statistical distance scale for planetary nebulae (PNe) based on a rigorous calibration sample. The distances of the calibration sample are derived from the trigonometric parallax method using the recent…

Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia EDR3, we generate a catalog of candidate double and multiply imaged lensed quasars and AGNs, comprising…

The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying…

We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in ${\it Gaia}$ DR2 data. Our algorithm detects overdensities in 4D position--proper motion space, making it the first search to explicitly use velocity…

The increasing availability and accessibility of numerous overhead images allows us to estimate and assess the spatial arrangement of groups of geospatial target objects, which can benefit many applications, such as traffic monitoring and…

We examine the capacity to identify binary systems from astrometric deviations alone. We apply our analysis to the Gaia eDR3 and DR2 data, specifically the Gaia Catalogue of Nearby Stars. We show we must renormalize (R)UWE over the local…

We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard \textit{ASTROSAT} and \textit{Gaia} EDR3. We used combinations of astrometric,…

Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques (e.g., machine-learning algorithms) available for the exploration of OCs. However, each method has its limitations and…

Aims: In this work, we aim to provide a reliable list of gravitational lens (GL) candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also show that the sole astrometric and photometric informations…

Open clusters (OCs) are regarded as tracers to understand stellar evolution theory and validate stellar models. In this study, we presented a robust approach to identifying OCs. A hybrid method of pyUPMASK and RF is first used to remove…

In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods,…

The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G ~ 21 mag. We showcase stellar variability across the Galactic colour-absolute magnitude diagram (CaMD), focusing on pulsating, eruptive, and…