We present a novel machine learning based approach for detecting galaxy-scale
gravitational lenses from interferometric data, specifically those taken with the International …

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 …

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

Classification is a widely used supervised learning technique that enables models to
discover the relationship between a set of features and a specified label using available …

Abstract The Shallow and the Deep is a collection of lecture notes that offers an accessible
introduction to neural networks and machine learning in general. However, it was clear from …

This paper explores how artificial intelligence (AI) techniques can address common
challenges in astronomy and (bio) medical imaging. It focuses on applying convolutional …

Blind spectrum sensing (BSS) is a valuable technique for identifying unknown signals in
scenarios where prior knowledge is limited. However, traditional methods encounter …

We present an analysis of the detection fraction and the number counts of radio sources
imaged with Very Long Baseline Interferometry (VLBI) at 1.4 GHz as part of the mJIVE-20 …

252 C. Heneka et al. through increasing amounts of data. For radio astronomical data this
prominently concerns the SKA (Braun et al. 2015) and its precursor instruments such as …

The use of artificial intelligence, more specifically of algorithms based on modern machine
learning and neural networks, has recently seen accelerated use in astronomy. At the very …