4 citations · 7 across the 3 of their papers we have counts for
3 papers
Morphology of Radio Sources in Representation Space
Nicolas Baron Perez, Marcus Brüggen, Luisa Lucie-Smith
Understanding radio source morphologies and their classification remains challenging. We previously developed a deep clustering method based on self-supervised learning to classify…
Classification of Radio Sources Through Self-Supervised Learning
Nicolas Baron Perez, Marcus Brüggen, Gregor Kasieczka +1
The morphology of radio galaxies is indicative of their interaction with their surroundings, among other effects. Since modern radio surveys contain a large number of radio sources…
The eROSITA Final Equatorial-Depth Survey (eFEDS): A Machine Learning Approach to Infer Galaxy Cluster Masses from eROSITA X-ray Images
Sven Krippendorf, Nicolas Baron Perez, Esra Bulbul +15
We develop a neural network based pipeline to estimate masses of galaxy clusters with a known redshift directly from photon information in X-rays. Our neural networks are trained u…