15 citations · 15 across the 1 of their papers we have counts for
3 papers
Exploring X-ray variability with unsupervised machine learning I. Self-organizing maps applied to XMM-Newton data
Miloš Kovačević, Mario Pasquato, Martino Marelli +3
XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patter…
Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network
Miloš Kovačević, Graziano Chiaro, Sara Cutini +1
The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray sources in its first 8 years of operation. More than 3000 of them are blazars. About 60 per cent of the F…
Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources
Miloš Kovačević, Graziano Chiaro, Sara Cutini +1
Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to deve…