5 citations · 10 across the 2 of their papers we have counts for
2 papers
astro-ph.IM2020★ 5 cited
Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +7
The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by…
astro-ph.IM2020★ 5 cited
Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data
Maurizio D'Addona, Giuseppe Riccio, Stefano Cavuoti +2
Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and fu…