219 citations · 1.1k across the 56 of their papers we have counts for
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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…
Statistical characterization and classification of astronomical transients with Machine Learning in the era of the Vera C. Rubin Observatory
M. Vicedomini, M. Brescia, S. Cavuoti +2
Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and mu…
Periodic Astrometric Signal Recovery through Convolutional Autoencoders
Michele Delli Veneri, Louis Desdoigts, Morgan A. Schmitz +8
Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orb…
Comparison of outlier detection methods on astronomical image data
Lars Doorenbos, Stefano Cavuoti, Massimo Brescia +2
Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsuperv…
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…