74 citations · 298 across the 10 of their papers we have counts for
10 papers
Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +8
Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Densi…
Evolution of galaxy size--stellar mass relation from the Kilo Degree Survey
N. Roy, N. R. Napolitano, F. La Barbera +16
We have obtained structural parameters of about 340,000 galaxies from the Kilo Degree Survey (KiDS) in 153 square degrees of data release 1, 2 and 3. We have performed a seeing con…
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
Massimo Brescia, Stefano Cavuoti, Valeria Amaro +4
Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the rec…
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…