Plasma Physics (physics.plasm-ph)

Abstract: Disorder-induced heating (DIH) prevents ultracold neutral plasma into electron strong coupling regime. Here we propose a scheme to suppress electronic DIH via optical lattice. We simulate the evolution dynamics of ultracold neutral plasma constrained by three-dimensional optical lattice using classical molecular dynamics method. The results show that for experimentally achievable condition, electronic DIH is suppressed by a factor of 1.3, and the Coulomb coupling strength can reach to 0.8 which is approaching the strong coupling regime. Suppressing electronic DIH via optical lattice may pave a way for the research of electronic strongly coupled plasma.

Abstract: Recently, quasi-stationary structures called $E \times B$ staircases were observed in gyrokinetic simulations, in all transport channels [Dif-Pradalier et al. Phys. Rev. Lett. 114, 085004 (2015)]. We present a novel analytical theory - supported by plasma fluid simulations - for the generation of density profile corrugations (staircase): Turbulent fluctuations self-organize to generate quasi-stationary radial modulations $\Delta \theta_k(r,t)$ of the transport crossphase $\theta_k$ between density and electric potential fluctuations. The radial modulations of the associated particle flux drive zonal corrugations of the density profile, via a modulational instability. In turn, zonal density corrugations regulate the turbulence via nonlinear damping of the fluctuations.

Abstract: Terahertz (THz) emissions from fast electron and ion currents driven in relativistic, femtosecond laser-foil interactions are examined theoretically. We first consider the radiation from the energetic electrons exiting the backside of the target. Our kinetic model takes account of the coherent transition radiation due to these electrons crossing the plasma-vacuum interface as well as of the synchrotron radiation due to their deflection and deceleration in the sheath field they set up in vacuum. After showing that both mechanisms tend to largely compensate each other when all the electrons are pulled back into the target, we investigate the scaling of the net radiation with the sheath field strength. We then demonstrate the sensitivity of this radiation to a percent-level fraction of escaping electrons. We also study the influence of the target thickness and laser focusing. The same sheath field that confines most of the fast electrons around the target rapidly sets into motion the surface ions. We describe the THz emission from these accelerated ions and their accompanying hot electrons by means of a plasma expansion model that allows for finite foil size and multidimensional effects. Again, we explore the dependencies of this radiation mechanism on the laser-target parameters. Under conditions typical of current ultrashort laser-solid experiments, we find that the THz radiation from the expanding plasma is much less energetic -- by one to three orders of magnitude -- than that due to the early-time motion of the fast electrons.

Abstract: We use machine learning models to predict ion density and electron temperature from visible emission spectra, in a high energy density pulsed-power-driven aluminum plasma, generated by an exploding wire array. Radiation transport simulations, which use spectral emissivity and opacity values generated using the collisional-radiative code PrismSPECT, are used to determine the spectral intensity generated by the plasma along the spectrometer's line of sight. The spectra exhibit Al-II and Al-III lines, whose line ratios and line widths vary with the density and temperature of the plasma. These calculations provide a 2500-size synthetic dataset of 400-dimensional intensity spectra, which is used to train and compare the performance of multiple machine learning models on a 3-variable regression task. The AutoGluon model performs best, with an R2-score of roughly 98% for density and temperature predictions. Simpler models (random forest, k-nearest neighbor, and deep neural network) also exhibit high R2-scores (>90%) for density and temperature predictions. These results demonstrate the potential of machine learning in providing rapid or real-time analysis of emission spectroscopy data in pulsed-power-driven plasmas.