From the 1 of 5 linked papers with an AI index.
5 papers
Improving reddening estimates for RR Lyrae stars in the Gaia bands: a machine learning approach to the PAC(Z) relation
A. Garofalo, T. Muraveva, L. Monti +3
The paper recalibrates extinction (reddening) relations for RR Lyrae stars in Gaia photometric bands using machine learning, providing new PAC and PACZ formulas for both fundamenta…
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…
The mass-metallicity relation at z>3 down to M_*= 10^4 M_Sun. A local perspective using the metallicity distribution of RR Lyrae stars
M. Bellazzini, T. Muraveva, A. Garofalo
The mass-metallicity relation (MZR) is a fundamental scale law of galaxies. It is observed to evolve with redshift in unresolved galaxies up to z>6. However, observational constrai…