4 citations · 4 across the 3 of their papers we have counts for
3 papers · 1 filter
The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning
L. Cabayol, M. Eriksen, J. Carretero +123
Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is need…
The PAU Survey: Background light estimation with deep learning techniques
Laura Cabayol-Garcia, Martin B. Eriksen, Àlex Alarcón +17
In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGnet, a deep neural network to predict t…
The PAU Survey: star-galaxy classification with multi narrow-band data
Laura Cabayol, Ignacio Sevilla-Noarbe, Enrique Fernández +18
Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In th…