4 papers
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…
The SARAO MeerKAT Galactic Plane Survey extended source catalogue
C. Bordiu, S. Riggi, F. Bufano +18
We present a catalogue of extended radio sources from the SARAO MeerKAT Galactic Plane Survey (SMGPS). Compiled from 56 survey tiles and covering approximately 500 deg across t…
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…