Simone Riggi
13 papers 34 citations h-index 3 2013–2026
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the...
Self-supervised learning for radio-astronomy source classification: a benchmark
The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional visual models...
RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe...
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
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 the vast amount...
Towards Porting Astrophysics Visual Analytics Services in the European Open Science Cloud
STRADAViT: Self-Supervised Domain Adaptation of Vision Transformer Backbones for Radio Astronomy
Next-generation radio astronomy surveys are delivering millions of resolved sources, yet scalable morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We...