74 citations · 298 across the 10 of their papers we have counts for
4 papers · 1 filter
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
M. Bilicki, H. Hoekstra, M. J. I. Brown +24
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited c…
Probability density estimation of photometric redshifts based on machine learning
Stefano Cavuoti, Massimo Brescia, Valeria Amaro +3
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological pro…
METAPHOR: Probability density estimation for machine learning based photometric redshifts
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +3
We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated throug…
Cooperative photometric redshift estimation
Stefano Cavuoti, Crescenzo Tortora, Massimo Brescia +5
In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Usi…