Related papers: Automatic classification of time-variable X-ray so…

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…

We have attempted to analyse all the available data taken by XMM-Newton as it slews between targets. This slew survey, the resultant source catalogue and the analysis procedures used are described in an accompanying paper. In this letter we…

Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low…

Measuring distances of cosmological sources such as galaxies, stars and quasars plays an increasingly critical role in modern cosmology. Obtaining the optical spectrum and consequently calculating the redshift as a distance indicator could…

We present an X-ray point-source catalog from the XMM-Large Scale Structure survey region (XMM-LSS), one of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) fields. We target the XMM-LSS region with $1.3$ Ms of new…

The XMM-Newton Survey Science Centre Consortium (SSC) develops software in close collaboration with the Science Operations Centre to perform a pipeline analysis of all XMM-Newton observations. In celebration of the 20th launch anniversary,…

The XMM-Large Scale Structure survey, covering an area of 11.1 sq. deg., contains more than 6000 X-ray point-like sources detected with XMM-Newton down to a flux of 3x10^-15 erg s^-1 cm^-2 in the [0.5-2] keV band, the vast majority of which…

Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has…

The Optical Monitor Catalogue of serendipitous sources (OMCat) contains entries for every source detected in the publicly available XMM-Newton Optical Monitor (OM) images taken in either the imaging or ``fast'' modes. Since the OM is…

Large time-domain sky surveys generate extensive multi-year catalogs of light curves in which scientifically valuable transients, such as supernovae (SNe), are vastly outnumbered by artifacts and routine star variability. While supervised…

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…

Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to flag astrophysically interesting events rapidly for followup…

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major…

Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, projected to…

The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopic traffic simulation, a clustering technique and a…

Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty…

Machine learning based approaches are emerging as very powerful tools for many applications including source classification in astrophysics research due to the availability of huge high quality data from different surveys in observational…

Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario in real applications such as anomaly/fraud detection, has long been considered a significant challenge in machine learning. Motivated by…

Temporal variability in flux and spectral shape is ubiquitous in the X-ray sky and carries crucial information about the nature and emission physics of the sources. The EPIC instrument on board the XMM-Newton observatory is the most…

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GLEAM-X, VLASS and LoTSS is growing rapidly.…