464 citations · 1.7k across the 55 of their papers we have counts for
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astro-ph.IM2022★ 15 cited
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…
astro-ph.IM2022
The origin of the unfocused XMM-Newton background, its variability and lessons learned for ATHENA
Fabio Gastaldello, Martino Marelli, Silvano Molendi +7
We analyzed the unexposed to the sky outFOV region of the MOS2 detector on board XMM-Newton covering 15 years of data amounting to 255 Ms. We show convincing evidence that the orig…