Contour Extraction of Inertial Confinement Fusion Images By Data Augmentation

X-Ray radiographs are one of the primary results from inertial confinement fusion (ICF) experiments. Issues such as scarcity of experimental data, high levels of noise in the data, lack of ground truth data, and low resolution of data limit the use of machine/deep learning for automated analysis of radiographs. In this work we combat these roadblocks to the use of machine learning by creating a synthetic radiograph dataset resembling experimental radiographs. Accompanying each synthetic radiograph are corresponding contours of each capsule shell shape, which enables neural networks to train on the synthetic data for contour extraction and be applied to the experimental images. Thus, we train an instance of the convolutional neural network U-Net to segment the shape of the outer shell capsule using the synthetic dataset, and we apply this instance of U-Net to a set of radiographs taken at the National Ignition Facility. We show that the network extracted the outer shell shape of a small number of capsules as an initial demonstration of deep learning for automatic contour extraction of ICF images. Future work may include extracting outer shells from all of the dataset, applying different kinds of neural networks, and extraction of inner shell contours as well.Comment: 6 pages, 9 figure

Halted-Pendulum Relaxation: Application to White Dwarf Binary Initial Data

Studying compact star binaries and their mergers is integral to modern astrophysics. In particular, binary white dwarfs are associated with Type Ia supernovae, used as standard candles to measure the expansion of the Universe. Today, compact-star mergers are typically studied via state-of-the-art computational fluid dynamics codes. One such numerical techniques, Smoothed Particle Hydrodynamics (SPH), is frequently chosen for its excellent mass, energy, and momentum conservation. Furthermore, the natural treatment of vacuum and the ability to represent highly irregular morphologies make SPH an excellent tool for the numerical study of compact-star binaries and mergers. However, for many scenarios, including binary systems, the outcome simulations are only as accurate as the initial conditions. For SPH, it is essential to ensure that particles are distributed semi-regularly, correctly representing the initial density profile. Additionally, particle noise in the form of high-frequency local motion and low-frequency global dynamics must be damped out. Damping the latter can be as computationally intensive as the actual simulation. Here, we discuss a new and straightforward relaxation method, Halted-Pendulum Relaxation (HPR), to remove the global oscillation modes of SPH particle configurations. In combination with effective external potentials representing gravitational and orbital forces, we show that HPR has an excellent performance in efficiently relaxing SPH particles to the desired density distribution and removing global oscillation modes. We compare the method to frequently used relaxation approaches such as gravitational glass, increased artificial viscosity, and Weighted Voronoi Tesselations, and test it on a white dwarf binary model at its Roche lobe overflow limit

Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics

Smoothed Particle Hydrodynamics (SPH) is a frequently applied tool in computational astrophysics to solve the fluid dynamics equations governing the systems under study. For some problems, for example when involving asteroids and asteroid impacts, the additional inclusion of material strength is necessary in order to accurately describe the dynamics. In compact stars, that is white dwarfs and neutron stars, solid components are also present. Neutron stars have a solid crust which is the strongest material known in nature. However, their dynamical evolution, when modeled via SPH or other computational fluid dynamics codes, is usually described as a purely fluid dynamics problem. Here, we present the first 3D simulations of neutron-star crustal toroidal oscillations including material strength with the Los Alamos National Laboratory SPH code FleCSPH. In the first half of the paper, we present the numerical implementation of solid material modeling together with standard tests. The second half is on the simulation of crustal oscillations in the fundamental toroidal mode. Here, we dedicate a large fraction of the paper to approaches which can suppress numerical noise in the solid. If not minimized, the latter can dominate the crustal motion in the simulations.Comment: 24 pages, 29 figure

TIP3P and TIP4P in Molecular Dynamics Simulations of Erythrocyte Aquaporins

Membrane protein simulation is vital for its vast implications in the study of biological processes. Because of this, curating the most accurate simulations that agree with in vitro experiments is of paramount importance. In membrane protein simulations concerning the permeability of a protein to water, implementing the most optimal water model and lipid bilayer constituents is a naturally arising concern. While water model TIP3P is conventionally used in molecular dynamics because of the optimization of its parameters to protein-protein interactions, many doubt the accuracy of simulations using TIP3P given that water model TIP4P outperforms TIP3P in certain regards. For instance, TIP4P yields considerably more accurate results in predicting bulk water diffusion constants. In addition to the choice of water model, membrane composition choice plays an important role. For instance, is it necessary to model the bilayer in accordance with experimentally validated compositions, and is it acceptable to use a bilayer consisting of one lipid type, as is done in the present literature? In our investigation, we sought to answer which of these two water models is better honed for permeability predictions of erythrocyte aquaporin, particularly AQP1 and AQP3. Concurrently, since TIP3P is refined for water-protein interactions and TIP4P is refined for water-water interactions, the model that provides the most accurate predictions could indicate the type of interactions that permeability depends on the most. We also sought to determine the necessity of implementing more accurate membrane models. We conducted molecular dynamics simulations of these variant water models and lipid compositions of AQP1 and AQP3 systems, using transition state theory to calculate the permeability, along with cell swelling assays to validate our results. Our results show that employing TIP3P and a more accurate lipid composition is preferable to using TIP4P or a single lipid type in the membrane

Plasma Image Classification Using Cosine Similarity Constrained CNN

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei, and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in investigating not only the underlying physics of the experiments but the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction, and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93% accurate binary classification to distinguish unstable columns from stable columns and 92% accurate five-way classification of a small, labeled data set which includes three classes corresponding to varying levels of kink instability.Comment: 16 pages, 12 figures, For submission to Journal of Plasma Physic