Deep Learning Based Detection of Cosmological Diffuse Radio Sources

Understand

In this paper we introduce a reliable, fully automated and fast algorithm to detect extended extragalactic radio sources (cluster of galaxies, filaments) in existing and forthcoming surveys (like LOFAR and SKA).

• The proposed solution is based on the adoption of a Deep Learning approach, more specifically a Convolutional Neural Network, that proved to perform outstandingly in the processing, recognition and classification of images.
• The challenge, in the case of radio interferometric data, is the presence of noise and the lack of a sufficiently large number of labeled images for the training.
• We have specifically addressed these problems and the resulting software, COSMODEEP proved to be an accurate, efficient and effective solution for detecting very faint sources in the simulated radio images.