1 citations · 1 across the 1 of their papers we have counts for
3 papers
astro-ph.IM2025
radio-llava: Advancing Vision-Language Models for Radio Astronomical Source Analysis
S. Riggi, T. Cecconello, A. Pilzer +5
The advent of next-generation radio telescopes is set to transform radio astronomy by producing massive data volumes that challenge traditional processing methods. Deep learning te…
astro-ph.IM2024★ 1 cited
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…
astro-ph.IM2024
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…