The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products
Astronomy & Astrophysics EDP Sciences 666 (2022) A120-A120
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 difficultAstrophysical gravitational-wave echoes from galactic nuclei
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 515:3 (2022) 3299-3318
A phase-shift-periodic parallel boundary condition for low-magnetic-shear scenarios
(2022)
Proton and Helium Ion Acceleration at Magnetotail Plasma Jets
Journal of Geophysical Research: Space Physics American Geophysical Union (AGU) 127:8 (2022) e2022JA030430
Abstract:
Gyrokinetic electrostatic turbulence close to marginality in the Wendelstein 7-X stellarator
Physical Review E American Physical Society 106 (2022) L013202