Publication

New PAU Paper

“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

Latest Group News

Conference

IFAE at the European Astronomical Society Annual Meeting (EAS 2022)

July 13, 2022

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Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument ” has been uploadd to arXiv. Several IFAE members are co-authors of the paper.

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The effect of quasar redshift errors on Lyman-α forest correlation functions ” by A. Font-Ribera, Ignasi Pérez-Ràfols, César Ramírez-Pérez and collaborators has been uploaded to arXiv.