37 citations · 115 across the 18 of their papers we have counts for
9 papers · 1 filter
ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey
Imdad Mahmud Pathi, John Y. H. Soo, Mao Jie Wee +16
ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift est…
The PAU Survey: Photometric Calibration of Narrow Band Images
F. J. Castander, S. Serrano, M. Eriksen +20
The Physics of the Accelerating Universe (PAU) camera is an optical narrow band and broad band imaging instrument mounted at the prime focus of the William Herschel Telescope. We d…
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: Narrow-band image photometry
S. Serrano, E. Gaztañaga, F. J. Castander +22
PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters…
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…