Related papers: Via Machinae: Searching for Stellar Streams using …

Streams of stars from captured dwarf galaxies and dissolved globular clusters are identifiable through the similarity of their orbital parameters, a fact that remains true long after the streams have dispersed spatially. We calculate the…

We present a panoramic map of the stellar streams of the Milky Way based upon astrometric and photometric measurements from the Gaia DR2 catalogue. In this first contribution, we concentrate on the halo at heliocentric distances beyond 5…

A method to search for tidal streams and to fit their orbits based on maximum likelihood is presented and applied to the Gaia data. Tests of the method are performed showing how a simulated stream produced by tidal stripping of a star…

MEDEA is a software architecture to detect luminosity variations connected with the discovery of new planet outside the Solar System. Taking into account the enormous number of stars to monitor for our aim traditional approaches are very…

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…

This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations…

We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ…

Stellar streams are sensitive probes of the Milky Way's gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise…

Nearby stellar streams carry unique information on the dynamical evolution and disruption of stellar systems in the Galaxy, the mass distribution in the disk, and provide unique targets for planet formation and evolution studies. We revisit…

In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release of Gaia (DR2). In this paper, we apply two clustering…

Gaia's milli-arcsec (mas) astrometric precision allows systematic identification of optically-selected sub-kpc dual active galactic nuclei (AGN), off-nucleus AGN, and small-scale lensed quasars by `varstrometry' -- where variability-induced…

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 Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources,…

Aims. To describe two potential options for the Source Environment Analysis pipeline, SEAPipe, for the Gaia mission. This pipeline will enable the discovery of sources which are new to Gaia, in the sense that they were not found by the…

Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight…

We present maps of the stellar streams detected in the Gaia Data Release 2 (DR2) and Early Data Release 3 (EDR3) catalogs using the STREAMFINDER algorithm. We also report the spectroscopic follow-up of the brighter DR2 stream members…

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine…

Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in…

Gaia will observe more than one billion objects brighter than V=20, including stars, asteroids, galaxies and quasars. As Gaia performs real time detection (i.e. without an input catalogue) the intrinsic properties of most of these objects…

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…