Variance-based data point selection combined with the SALI neural network reduces nuclear spin characterization time by 85% in experiment (high field) and predicts 60% savings in simulation (low field).
Turbulence and magnetic spots at the surface of hot massive stars
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abstract
Hot luminous stars show a variety of phenomena in their photospheres and in their winds which still lack clear physical explanations at this time. Among these phenomena are non-thermal line broadening, line profile variability (LPVs), discrete absorption components (DACs), wind clumping and stochastically excited pulsations. Cantiello et al. (2009) argued that a convection zone close to the surface of hot, massive stars, could be responsible for some of these phenomena. This convective zone is caused by a peak in the opacity due to iron recombination and for this reason is referred as the "iron convection zone" (FeCZ). 3D MHD simulations are used to explore the possible effects of such subsurface convection on the surface properties of hot, massive stars. We argue that turbulence and localized magnetic spots at the surface are the likely consequence of subsurface convection in early type stars.
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Reducing Sensing Time through Offline Experimental Design for Nuclear Spin Detection
Variance-based data point selection combined with the SALI neural network reduces nuclear spin characterization time by 85% in experiment (high field) and predicts 60% savings in simulation (low field).