We present DECORAS, a deep-learning-based approach to detect both point and extended
sources from Very Long Baseline Interferometry (VLBI) observations. Our approach is based …

Point source detection at low signal-to-noise ratio (SNR) is challenging for astronomical
surveys, particularly in radio interferometry images where the noise is correlated. Machine …

We present a deep learning (DL) pipeline developed for the detection and characterization
of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array …

Context. Rising interest in radio astronomy and upcoming projects in the field is expected to
produce petabytes of data per day, questioning the applicability of traditional radio …

Source finding is one of the most challenging tasks in upcoming radio continuum surveys
with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the …

In this paper we introduce a reliable, fully automated and fast algorithm to detect extended
extragalactic radio sources (cluster of galaxies, filaments) in existing and forthcoming …

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve
previously undetected extended and diffuse emissions, which brings great challenges for …

We present two new source extraction methods, based on Bayesian model selection and
using the Bayesian information criterion. The first is a source detection filter, which is able to …

Modern high-sensitivity radio telescopes are discovering an increased number of resolved
sources with intricate radio structures and fainter radio emissions. These sources often …

Large surveys using modern telescopes are producing images that are increasing
exponentially in size and quality. Identifying objects in the generated images by visual …