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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…
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…
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…
DAMEWARE - Data Mining & Exploration Web Application Resource
Massimo Brescia, Stefano Cavuoti, Francesco Esposito +7
Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application a…