2 citations · 3 across the 4 of their papers we have counts for
4 papers
Variable Star Light Curves in Koopman Space
Nicolas Mekhaël, Mario Pasquato, Gaia Carenini +4
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variabl…
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato, Piero Trevisan, Abbas Askar +4
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle pred…
Sparse logistic regression for RR Lyrae vs binaries classification
Piero Trevisan, Mario Pasquato, Gaia Carenini +4
RR Lyrae (RRL) are old, low-mass radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators, since they…
Measuring the spectral index of turbulent gas with deep learning from projected density maps
Piero Trevisan, Mario Pasquato, Alessandro Ballone +1
Turbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of the…