The amount and complexity of data delivered by modern galaxy surveys has been steadily
increasing over the past years. New facilities will soon provide imaging and spectra of …

In recent years, deep learning has been successfully applied in various scientific domains.
Following these promising results and performances, it has recently also started being …

Multi-colour photometry sky survey projects would obtain celestial object images of different
colours with a wide field telescope and several different filters. Images of different colours …

Stellar classification is a central topic in astronomical research that relies mostly on the use
of spectra. However, with the development of large sky surveys, spectra are becoming …

In this paper, we propose a convolutional neural network (CNN)-based photometric pipeline
for the Sloan Digital Sky Survey (SDSS) images. The pipeline includes three main parts: the …

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

Context. Precise continuum normalisation of merged échelle spectra is a demanding task
that is necessary for various detailed spectroscopic analyses. Automatic methods have …

The morphological diversity of galaxies is a relevant probe of galaxy evolution and
cosmological structure formation, but the classification of galaxies in large sky surveys is …

We explore the use of deep learning to localise galactic structures in low surface brightness
(LSB) images. LSB imaging reveals many interesting structures, though these are frequently …

In the context of next-generation radio-astronomical visual surveys, automated object
detection and segmentation are necessary tasks to support astrophysics research from …