Theoretical astrophysics and plasma physics at RPC

Abstract:

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult

Abstract:

The transition from strong (fluidlike) to nearly marginal (Floquet-type) regimes of ion-temperature-gradient (ITG) driven turbulence is studied in the stellarator Wendelstein 7-X by means of numerical simulations. Close to marginality, extended (along magnetic field lines) linearly unstable modes are dominant, even in the presence of kinetic electrons, and provide a drive that results in finite turbulent transport. A total suppression of turbulence above the linear stability threshold of the ITG modes, commonly present in tokamaks and known as the “Dimits shift,” is not observed. We show that this is mostly due to the peculiar radial structure of marginal turbulence, which is more localized than in the fluid case and therefore less likely to be stabilized by shearing flows.