8 citations · 19 across the 11 of their papers we have counts for
5 papers · 1 filter
Self-supervised learning for radio-astronomy source classification: a benchmark
Thomas Cecconello, Simone Riggi, Ugo Becciani +5
The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional…
Detection and classification of radio sources with deep learning
S. Riggi, T. Cecconello, U. Becciani +1
In this paper we present three different applications, based on deep learning methodologies, that we are developing to support the scientific analysis conducted within the ASKAP-EM…
Sifting the debris: Patterns in the SNR population with unsupervised ML methods
F. Bufano, C. Bordiu, T. Cecconello +13
Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, m…
Self-supervised contrastive learning of radio data for source detection, classification and peculiar object discovery
S. Riggi, T. Cecconello, S. Palazzo +14
New advancements in radio data post-processing are underway within the SKA precursor community, aiming to facilitate the extraction of scientific results from survey images through…
Classification of compact radio sources in the Galactic plane with supervised machine learning
S. Riggi, G. Umana, C. Trigilio +13
Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and it…