Solar twins are stars that have spectra very similar to the Sun, with effective temperature, surface gravity and metallicity around solar values. This similarity allow us to determine very precise stellar parameters and chemical abundances (~0.01 dex), that makes possible the investigation of effects that can imprint subtle changes in the chemical pattern of a star, for example planet engulfments.Also, the high precision atmospheric parameters that can be derived for these objects permit us a reliable determination of their ages using a traditional isochrone method which, in association with the abundances determination, can bring many benefits to studies of the chemical evolution of the Galaxy.I will talk about the differential method, and discuss about past and recent works on the planet-host star chemical connection and the chemical evolution of the Galaxy.
More than one-third of the 4000+ planet candidates found by NASA’s Kepler spacecraft are associated with target stars that have more than one planet candidate, and such “multis” account for the vast majority of candidates that have been verified as true planets.The large number of multis tells us that flat multiplanet systems like our Solar System are common. Virtually all of the candidate planetary systems are stable, as tested by numerical integrations that assume a physically motivated mass-radius relationship. Statistical studies performed on these candidate systems reveal a great deal about the architecture of planetary systems, including the typical spacing of orbits and flatness. The characteristics of several of the most interesting confirmed Kepler & K2 multi-planet systems will also be discussed.
Dealing with large amount of data is a new problematic task in astrophysics. One may distinguish the management of these data (astroinformatics) and their scientific use (astrostatistics) even if the border is rather fuzzy. Dimensionality reduction in both the number of observations and the number of variables (observables) is necessary for an easier physical understanding. This is the purpose of classification which has been traditionally eye-based and essentially still is but this becomes not possible anymore. In this talk, I present a general overview of machine learning approaches for unsupervised classification, with applications to stars (chemical abundances) and galaxies (spectra).