8 citations · 19 across the 11 of their papers we have counts for
12 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…
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…
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
Renato Sortino, Thomas Cecconello, Andrea DeMarco +10
Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from…
Radio source analysis services for the SKA and precursors
Simone Riggi, Cristobal Bordiu, Daniel Magro +8
New developments in data processing and visualization are being made in preparation for upcoming radioastronomical surveys planned with the Square Kilometre Array (SKA) and its pre…
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…
Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud
T. Cecconello, C. Bordiu, F. Bufano +6
Extracting information from raw data is probably one of the central activities of experimental scientific enterprises. This work is about a pipeline in which a specific model is tr…
Novel EOSC Services for Space Challenges: The NEANIAS First Outcomes
Eva Sciacca, Mel Krokos, Ugo Becciani +13
The European Open Science Cloud (EOSC) initiative faces the challenge of developing an agile, fit-for-purpose, and sustainable service-oriented platform that can address the evolvi…