Related papers: Unsupervised learning for variability detection wi…
Open clusters are key targets for both Galaxy structure and evolution and stellar physics studies. Since \textit{Gaia} DR2 publication, the discovery of undetected clusters has proven that our samples were not complete. Our aim is to…
Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a…
Stellar variability studies are now reaching a completely new level thanks to ESA's Gaia mission, which enables us to locate many variable stars in the Hertzsprung-Russell diagram and determine the various instability strips/bands.…
We present a method to identify likely visual binaries in Gaia eDR3 that does not rely on parallax or proper motion. This method utilizes the various PSF sizes of 2MASS/Gaia, where at $<2.5$" two stars may be unresolved in 2MASS but…
Gaia DR3, released in June 2022, included low-resolution XP spectra that have been used for the classification of various types of emission-line objects through machine-learning techniques. The Gaia Extended Stellar Parametrizer for…
Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with…
The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in Astronomy. It includes precise astrometric data (positions, proper motions and parallaxes) for more than $1.3$ billion sources, mostly stars. To analyse such a vast…
Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to…
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation,…
We combine Gaia Data Release 3 and artificial intelligence to enhance the current statistics of substellar companions, particularly within regions of the orbital period vs. mass parameter space that remain poorly constrained by the radial…
We present a new framework to detect various types of variable objects within massive astronomical time-series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a…
The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that…
Different stellar populations may be identified through differences in chemical, kinematic, and chronological properties, suggesting the interplay of various physical mechanisms that led to their origin and subsequent evolution. As such,…
Context: Photometric variability is a defining characteristic of young stellar objects (YSO), which can be traced back to a range of physical processes taking place at different stages of young stars' formation and early evolution. Gaia's…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
At the core of the Gaia mission is a multi-epoch survey consisting of astrometric, photometric, spectrophotometric, and spectroscopic measurements. The astrometric time series provides parallaxes and proper motions, along with information…
Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…
The unsupervised 3D object detection is to accurately detect objects in unstructured environments with no explicit supervisory signals. This task, given sparse LiDAR point clouds, often results in compromised performance for detecting…
In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this…
To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D object" dataset by using part contrasting and object…