7 citations · 10 across the 7 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Astronomical source detection in radio continuum maps with deep neural networks
S. Riggi, D. Magro, R. Sortino +13
Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Austra…