This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives.

In recent work, we have demonstrated the capability of directional beam forming in the microwave portion of the electromagnetic spectrum using linear superconducting ring arrays, triggered by an ultra-fast laser pulse. This paper extends these developments by presenting modeling of microwave radiation patterns covering the entire four pi steradian sphere, and outlining an experimental plan for detailed characterization of planar arrays. Experimental data is compared with the simulations conducted in this study, showing good agreement that builds confidence for further work. Planned research is presented to include array design optimizations for varying frequencies into the THz region. Additional research will include experimental and computational investigation of THz propagation using previously developed laser atmospheric propagation software. If successful, the numerical models used could prove beneficial in predicting real time absorption characteristics.

In this study, we consider three different machine-learning methods-a three-hidden-layer neural network, support vector regression, and Gaussian process regression-and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine-learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine-learning model we considered, support vector regression performed very well in our tests.

We present detailed characterization of laser-driven fusion and neutron production (similar to 10(5)/second) using 8 mJ, 40 fs laser pulses on a thin (<1 mu m) D2O liquid sheet employing a measurement suite. At relativistic intensity (similar to 5x10(18 )W/cm(2)) and high repetition rate (1 kHz), the system produces deuterium-deuterium (D-D) fusion, allowing for consistent neutron generation. Evidence of D-D fusion neutron production is verified by a measurement suite with three independent detection systems: an EJ-309 organic scintillator with pulse-shape discrimination, a He-3 proportional counter and a set of 36 bubble detectors. Time-of-flight analysis of the scintillator data shows the energy of the produced neutrons to be consistent with 2.45 MeV. Particle-in-cell simulations using the WarpX code support significant neutron production from D-D fusion events in the laser-target interaction region. This high-repetition-rate laser-driven neutron source could provide a low-cost, on-demand test bed for radiation hardening and imaging applications.

Researchers in the field of ultra-intense laser science are beginning to embrace machine learning methods. In this study we consider three different machine learning methods -- a two-hidden layer neural network, Support Vector Regression and Gaussian Process Regression -- and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study we focus on both the accuracy of the machine learning methods and the performance on one GPU including the memory consumption. Although it is arguably the least sophisticated machine learning model we considered, Support Vector Regression performed very well in our tests.

We examine the electromagnetic emission from two photo-illuminated linear arrays composed of inductively charged superconducting ring elements. The arrays are illuminated by an ultrafast infrared laser that triggers microwave broadband emission detected in the 1–26 GHz range. Based on constructive interference from the arrays a narrowing of the forward radiation lobe is observed with increasing element count and frequency demonstrating directed GHz emission. Results suggest that higher frequencies and a larger number of elements are achievable leading to a unique pulsed array emitter concept that can span frequencies from the microwave to the terahertz (THz) regime.

The design and initial experimental results from an event-based, rotating-polarizer, imaging polarimeter are presented. The speed of division-of-time imaging systems is traditionally constrained by the frame rate of the focal plane sensor and the need to sample several polarization angles during rotation. The asynchronous event reporting of event-based cameras (EBCs) frees these constraints but introduces challenges. The Stokes vectors used to describe polarization are based on irradiance, but EBCs are inherently change-detection systems that struggle to estimate irradiance. Two methods of estimating the polarization state without first recovering irradiance images are presented and compared. The DAVIS 346 sensor enables simultaneous recording of events with conventional frame-based images, enabling direct comparison of event-based polarization estimates to traditional techniques. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The impact of the physical geometry of source versus target produced by a nuclear device used for neutron irradiation and energy deposition as a means of asteroid deflection is investigated. This work examines how these geometric profiles change as both the detonation distance and the neutron source energy is changed. Additionally, the effects of profile shaping on asteroid deflection scenarios is investigated. A comparison is made between current approaches for approximating neutron energy deposition in asteroids using direct Monte-Carlo simulations to generate high-fidelity spatial energy deposition profiles. A detonation source is generated at stand-off distances equal to 0.0001, 0.5, and 5 times the radius for a 500-meter-diameter notional silicon-dioxide asteroid target. Two mono-energetic neutron sources, 14.1 MeV deuterium-tritium fusion neutrons, and 1 MeV Watt-fission neutrons, were modeled to explore the impact of neutron energy on the energy deposition profile and resulting material blow-off for asteroid deflection scenarios. Comparison of the resulting source-particle normalized energy deposition profiles reveals that the distributions vary significantly as the neutron energies and distances change. Hydrodynamic results of the asteroid response indicate that the varying energy deposition can significantly affect overall asteroid deflection, thereby suggesting that tailored neutron spectra could be employed to enhance overall asteroid deflection.

: The purpose of this research was to produce and characterize high repetition rate terahertz radiation in ionized air plasma. An 800 nanometer, 50 femtosecond, 0.35 Watt, 40 KHz, pulsed Ti:Sapphire laser system was used as the source infrared beam. This beam was focused onto a second harmonic generation crystal to produce a collinear, perpendicularly polarized secondary beam at 400 nm. After realigning the polarization of the fundamental to the second harmonic and compensating for group velocity dispersion introduced by the optics, both beams were recombined and focused by a 3.75 cm focal length mirror to form an air plasma. An electrical bias of up to 27 kV/cm was applied across the plasma to enhance the terahertz and plasma signal. The air plasma intensity was measured using a 40 kHz ultrasonic transducer, while the terahertz radiation was measured by a silicon bolometer. Terahertz of reasonable power was detected and characterized using this method. Both the terahertz and the plasma were characterized concurrently throughout this experiment in an effort to determine the feasibility of using the ultrasonic transducer as an alignment aid. The characterization included the effects of polarization, bias, and delay on the plasma and terahertz signals. During the course of this research it was also discovered that silicon, often used as a filter to isolate the detector from the laser in terahertz research, was a significant source of terahertz as well. This experiment represents the first time THz was generated using the two color interaction modified by an external applied bias.