37 citations · 37 across the 1 of their papers we have counts for
9 papers
Euclid preparation: X. The Euclid photometric-redshift challenge
Euclid Collaboration, G. Desprez, S. Paltani +170
Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. Howev…
The PAU Survey: Photometric redshifts using transfer learning from simulations
M. Eriksen, A. Alarcon, L. Cabayol +15
In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS fie…
CosmoHub: Interactive exploration and distribution of astronomical data on Hadoop
Pau Tallada, Jorge Carretero, Jordi Casals +13
We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmol…
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 Physics of the Accelerating Universe Camera
Cristobal Padilla, Francisco J. Castander, Alex Alarcon +30
The PAU (Physics of the Accelerating Universe) Survey goal is to obtain photometric redshifts (photo-z) and Spectral Energy Distribution (SED) of astronomical objects with a resolu…
The PAU Survey: Operation and orchestration of multi-band survey data
Nadia Tonello, Pau Tallada, Santiago Serrano +14
The Physics of the Accelerating Universe (PAU) Survey is an international project for the study of cosmological parameters associated with Dark Energy. PAU's 18-CCD camera (PAUCam)…