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

We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams within Gaia, using only angular positions, proper motions, and…

We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via Machinae 3.0: a re-optimized version of the stellar stream…

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along…

We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly supervised machine learning algorithm that builds a background…

The Gaia mission has led to the discovery of over 100 stellar streams in the Milky Way, most of which likely originated from globular clusters (GCs). As the upcoming wide-field surveys can potentially continue to increase the number of…

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels…

STREAMFINDER is a new algorithm that we have built to detect stellar streams in an automated and systematic way in astrophysical datasets that possess any combination of positional and kinematic information. In Paper I, we introduced the…

The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based…

Stellar streams provide one of the most promising avenues for constraining the global mass distribution of the Milky Way and the nature of dark matter (DM). The stream stars' kinematic "track" enables inference of large-scale properties of…

The lack of tangible evidence for non-gravitational interactions between dark and visible sectors drives the need for exploring new avenues of inferring dark matter properties through purely gravitational probes. In particular, addressing…

The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the…

The Gaia astrometric mission may offer an unprecedented opportunity to discover new tidal streams in the Galactic halo. To test this, we apply nGC3, a great-circle-cell count method that combines position and proper motion data to identify…

The identification of stellar structures in the Galactic halo, including stellar streams and merger remnants, often relies on the dynamics of their constituent stars. However, this approach has limitations due to the complex dynamical…

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.…

The hierarchical model of galaxy formation predicts that the Milky Way halo is populated by tidal debris of dwarf galaxies and globular clusters. Due to long dynamical times, debris from the lowest mass objects remains coherent as thin and…

We present an atlas and follow-up spectroscopic observations of 87 thin stream-like structures detected with the STREAMFINDER algorithm in Gaia DR3, of which 29 are new discoveries. Here we focus on using these streams to refine mass models…

We apply the automatic stellar stream detection algorithm StarStream to Gaia Data Release 3 and identify 87 stellar streams associated with Galactic globular clusters (GCs), including 34 high-quality cases with median completeness and…

The unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional…

This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys:…

The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the…