“The PAU Survey: Photometric redshifts using transfer learning from simulations” by M. Eriksen et al. has been uploaded to arXiv.
It is the first paper demonstrating we can constrain PAUS redshifts with deep learning techniques. Previously we had (Eriksen 2019) shown it worked with template fitting method. Using a deep neural network we managed to improvethe photo-z scatter with 50% for the faintest galaxies. This was possible througha set of different techniques introduced in the paper. Among the most important was combining simulated and observed data when training the neural network.We also included techniques like auto-encoders to extract information about the galaxy SEDs.
Link to arXiv
Publication
New Cosmology Group Publication
April 14, 2021
“The PAU survey: Estimating galaxy photometry with deep learning” by L. Cabayol et al. has been submitted to MNRAS.
IFAE in the media
Latest eBoss results published in Investigación y Ciencia
January 13, 2021
The outreach article “El mayor mapa tridimensional del universo” by Andreu Font-Ribera and collaborators at eBoss has been published in the latest issue of Investigación y Ciencia.
Publication
New Comology group Publicaton
November 11, 2020
“Dark Energy Survey Year 3 Results: Weak Lensing Shape Catalogue”, led by Marco Gatti, has been uploaded to arXiv as part of the upcoming DES year 3 cosmology analysis of galaxy clustering and gravitational lensing.