Magnetic field induced transition in a wide parabolic well superimposed with superlattice

We study a $Al_{x}Ga_{x-1}As$ parabolic quantum wells (PQW) with $GaAs/Al_{x}Ga_{x-1}As$ square superlattice. The magnetotransport in PQW with intentionally disordered short-period superlattice reveals a surprising transition from electrons distribution over whole parabolic well to independent-layer states with unequal density. The transition occurs in the perpendicular magnetic field at Landau filling factor $\nu\approx3$ and is signaled by the appearance of the strong and developing fractional quantum Hall (FQH) states and by the enhanced slope of the Hall resistance. We attribute the transition to the possible electron localization in the x-y plane inside the lateral wells, and formation of the FQH states in the central well of the superlattice, driven by electron-electron interaction.Comment: 5 pages, 4 figure

Interplay of the exciton and electron-hole plasma recombination on the photoluminescence dynamics in bulk GaAs

We present a systematic study of the exciton/electron-hole plasma photoluminescence dynamics in bulk GaAs for various lattice temperatures and excitation densities. The competition between the exciton and electron-hole pair recombination dominates the onset of the luminescence. We show that the metal-to-insulator transition, induced by temperature and/or excitation density, can be directly monitored by the carrier dynamics and the time-resolved spectral characteristics of the light emission. The dependence on carrier density of the photoluminescence rise time is strongly modified around a lattice temperature of 49 K, corresponding to the exciton binding energy (4.2 meV). In a similar way, the rise-time dependence on lattice temperature undergoes a relatively abrupt change at an excitation density of 120-180x10^15 cm^-3, which is about five times greater than the calculated Mott density in GaAs taking into account many body corrections.Comment: 15 pages, 7 figures, submitted to Phys. Rev.

Application of Genetic Programming and Artificial Neural Network Approaches for Reconstruction of Turbulent Jet Flow Fields

Two Machine Learning (ML) methods are considered for reconstruction of turbulet signals corresponding to the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions of NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The first method is the Feedforward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in (Lui and Wolf, 2019). The second method is based on the application of Genetic Programming, which is well-known in optimisation research, but has not been applied for turbulent flow reconstruction before. The reconstruction of local flow velocity and pressure signals as well as timedependent principle coefficients of the Spectral Proper Orthogonal Decomposition of turbulent pressure fluctuations are considered. Stability and dependency of the ML algorithms on the smoothness property and the sampling rate of the underlying turbulent flow signals are discussed