5 papers
CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Galactic Archaeology and Scientific Discovery
Lorenzo Monti, Tatiana Muraveva, Brian Sheridan +4
In data-driven scientific discovery, a challenge lies in classifying well-characterized phenomena while identifying novel anomalies. Current semi-supervised clustering algorithms d…
Unified Deep Learning Approach for Estimating the Metallicities of RR Lyrae Stars Using light curves from Gaia Data Release 3
Lorenzo Monti, Tatiana Muraveva, Alessia Garofalo +2
RR Lyrae stars (RRLs) are old pulsating variables widely used as metallicity tracers due to the correlation between their metal abundances and light curve morphology. With ESA Gaia…
Exploring the Sagittarius stream with RR Lyrae Stars from Gaia Data Release 3
Tatiana Muraveva, Michele Bellazzini, Alessia Garofalo +3
The Sagittarius (Sgr) dwarf spheroidal galaxy is one of the most prominent satellites of the Milky Way (MW). It is currently undergoing tidal disruption, forming an extensive stell…
Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars
Lorenzo Monti, Tatiana Muraveva, Gisella Clementini +1
Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast d…
Metallicity of RR Lyrae stars from the Gaia Data Release 3 catalogue computed with Machine Learning algorithms
Tatiana Muraveva, Andrea Giannetti, Gisella Clementini +2
We present new and relations for fundamental-mode (RRab) and first-overtone mode (RRc) RR Lyrae stars (RRLs), respectivel…