From the 2 of 27 linked papers with an AI index.
1 citations · 1 across the 10 of their papers we have counts for
6 papers · 1 filter
STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer
Andrea DeMarco, Ian Fenech Conti, Hayley Camilleri +2
Next-generation radio astronomy surveys are delivering millions of resolved sources, but robust and scalable morphology analysis remains difficult across heterogeneous telescopes a…
Continuous Wide-Field Optical Monitoring for Very Early-Phase Transient Discovery
Massimo Della Valle, Maria Teresa Botticella, Enrico Cappellaro +40
The study of transient phenomena in a multimessenger context is expected to remain a major pillar of astrophysical discovery in the decades ahead. Supernovae, Kilonovae, Black-Hole…
Solar flare forecasting with foundational transformer models across image, video, and time-series modalities
S. Riggi, P. Romano, A. Pilzer +1
We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-serie…
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…