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Shock-bubble interactions (SBI) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major obstacle in achieving ignition. Experiments imaging the collapse of such voids at high energy densities (HED) are constrained by spatial and temporal resolution, making simulations a vital tool in understanding these systems. In this study, we benchmark several radiation and thermal transport models in the xRAGE hydrodynamic code against experimental images of a collapsing mesoscale void during the passage of a 300 GPa shock. We also quantitatively examine the role of transport physics in the evolution of the SBI. This allows us to understand the dynamics of the interaction at timescales shorter than experimental imaging framerates. We find that all radiation models examined reproduce empirical shock velocities within experimental error. Radiation transport is found to reduce shock pressures by providing an additional energy pathway in the ablation region, but this effect is small ($\sim$1\% of total shock pressure). Employing a flux-limited Spitzer model for heat conduction, we find that flux limiters between 0.03 and 0.10 produce agreement with experimental velocities, suggesting that the system is well-within the Spitzer regime. Higher heat conduction is found to lower temperatures in the ablated plasma and to prevent secondary shocks at the ablation front, resulting in weaker primary shocks. Finally, we confirm that the SBI-driven instabilities observed in the HED regime are baroclinically driven, as in the low energy case., Comment: 14 pages, 12 figures

The last decade has shown the great potential that X-ray Free Electron Lasers (FEL) have to study High Energy Density (HED) matter. Experiments at FELs have made significant breakthroughs in Shock Physics and Dynamic Diffraction, Dense Plasma Physics and Warm Dense Matter Science, using techniques such as isochoric heating, inelastic scattering, small angle scattering and x-ray diffraction. In addition, and complementary to these techniques, the coherent properties of the FEL beam can be used to image HED samples with high fidelity. We present new imaging diagnostics and techniques developed at the Matter in Extreme Conditions (MEC) instrument at Linac Coherent Light Source (LCLS) over the last few years. We show results in Phase Contrast Imaging geometry, where the X-ray beam propagates from the target to a camera revealing its phase, as well as in Direct Imaging geometry, where a real image of the sample plane is produced in the camera with a spatial resolution down to 200 nm. Last, we show an implementation of the Talbot Imaging method allowing both x-ray phase and intensity measurements change introduced by a target with sub-micron resolution.

We report on a novel approach to dynamic compression of materials that bridges the gap between previous static- and dynamic- compression techniques, allowing to explore a wide range of pathways in the pressure-temperature space. By combining a dynamic-diamond anvil cell setup with double-sided laser-heating and in situ X-ray diffraction, we are able to perform dynamic compression at high temperature and characterize structural transitions with unprecedented time resolution. Using this method, we investigate the $\gamma-\epsilon$ phase transition of iron under dynamic compression for the first time, reaching compression rates of hundreds of GPa/s and temperatures of 2000 K. Our results demonstrate a distinct response of the $\gamma-\epsilon$ and $\alpha-\epsilon$ transitions to the high compression rates achieved. These findings open up new avenues to study tailored dynamic compression pathways in the pressure-temperature space and highlight the potential of this platform to capture kinetic effects in a diamond anvil cell., Comment: Reworked the text and figures to be more in line with the format of PRL

In this work we present a detailed structural characterization of Si-4H, a newly discovered bulk form of hexagonal silicon (Si) with potential optoelectronic applications. Using multi-scale imaging, we reveal a hierarchical structure in the morphology of Si-4H obtained from high-pressure synthesis. We demonstrate discrete structural units, platelets, at an intermediate length-scale between the bulk pellets synthesized at high pressures and the flake-like crystallites inferred in previous studies. Direct observation of the platelets reveals their 2D structure, with planar faces spanning hundreds of nanometers to a few micrometers and thicknesses of only tens of nanometers. We separated and dispersed small packets of quasi-single platelets, which enabled us to analyze the crystalline domains within each grain. With this view, we demonstrate that Si-4H platelets represent the smallest crystalline structural units, which can bend at the single-domain level. Our characterization of the quasi-2D, flexible platelets of hexagonal Si-4H and proof of concept that the platelets can be dispersed and manipulated quite simply demonstrate opportunities to design novel optoelectronic and solar devices.

We propose an inferential approach for maximum likelihood estimation of the hidden Markov models for continuous responses. We extend to the case of longitudinal observations the finite mixture model of multivariate Gaussian distributions with Missing At Random (MAR) outcomes, also accounting for possible dropout. The resulting hidden Markov model accounts for different types of missing pattern: (i) partially missing outcomes at a given time occasion; (ii) completely missing outcomes at a given time occasion (intermittent pattern); (iii) dropout before the end of the period of observation (monotone pattern). The MAR assumption is formulated to deal with the first two types of missingness, while to account for informative dropout we assume an extra absorbing state. Maximum likelihood estimation of the model parameters is based on an extended Expectation-Maximization algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.

Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structures in the data. Such extensions lead to a very flexible class of models, which allows us to fit different types of longitudinal data. Model parameters are estimated through the expectation-maximization algorithm, based on the forward-backward recursions, which is implemented in the main functions of the package. The package also allows us to perform local and global decoding and to obtain standard errors for the parameter estimates. We illustrate its use and the most important features on the basis of examples involving applications in health and criminology.