18 citations · 27 across the 4 of their papers we have counts for
4 papers
STRADAViT: Self-Supervised Domain Adaptation of Vision Transformer Backbones for Radio Astronomy
Andrea DeMarco, Ian Fenech Conti, Hayley Camilleri +2
Next-generation radio astronomy surveys are delivering millions of resolved sources, yet scalable morphology analysis remains difficult across heterogeneous telescopes and imaging…
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
Renato Sortino, Daniel Magro, Giuseppe Fiameni +9
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 e…
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…
A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing
Daniel Magro, Kristian Zarb Adami, Andrea DeMarco +2
As we enter the era of large-scale imaging surveys with the up-coming telescopes such as LSST and SKA, it is envisaged that the number of known strong gravitational lensing systems…