The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will revolutionize our current astrophysical and cosmological knowledge of the Universe thanks to its long-term, sensitive, multi-band photometry. This experiment will collect data in six different photometric (ugrizy) bands, over a time scale of ten years. LSST's field of view is almost ten square degrees and it will scan the entire Southern sky approximately every three nights reaching r~24 mag per single exposure.
The early LSST data releases will be a goldmine to characterize resolved stellar populations in the Local Universe and to constrain the early formation and evolution of the Milky Way and nearby stellar systems. LSST will allow us to identify and characterize all southern stellar systems thus providing the largest demographic investigations of resolved stellar populations and to characterize periodic and aperiodic astrophysical phenomena ranging from tens of seconds to years.
Searching through the avalanche of data acquired by LSST will be a major challenge: it is expected that ~100 petabytes of data will be collected at the end of the experiment. The identification and characterization of variable stars and all kinds of stellar populations will require sophisticated techniques of Artificial Intelligence/Machine Learning (AI/ML). We are entering a new era in which a sophisticated data management system, the Rubin Science Platform, will enable database queries from individual astronomers to use the entire data set collected by LSST.
The VEra Rubin observatorY: sTars and stellAr Systems (VERYTAS) program will bring together researchers from four different astrophysical communities:
a) -- Stellar astrophysicists dealing with periodic and aperiodic variable stars.
b) -- Astrophysicists interested in using chemistry, photometry and kinematics to constrain the assembly history of nearby stellar systems.
c) -- Cosmologists dealing with numerical simulations including dark matter, star and gas components.
d) -- Astrophysicists interested in developing new algorithms and new AI/ML methodologies to efficiently and accurately mine the LSST data base.