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NASA NTRS2015

Desensitized Optimal Filtering and Sensor Fusion Toolkit

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Karlgaard, Christopher D.↗

DOE OSTI2022

Coherent manipulation of graph states composed of finite-energy Gottesman-Kitaev-Preskill-encoded qubits

Graph states are a central resource in measurement-based quantum information processing. In the photonic qubit architecture based on Gottesman-Kitaev-Preskill (GKP) encoding, the generation of high-fidelity graph states composed of realistic, finite-energy approximate GKP-encoded qubits thus constitutes a key task. We consider the finite-energy approximation of GKP-qubit states given by a coherent superposition of shifted finite-squeezed vacuum states, where the displacements are Gaussian distributed. We present an exact description of graph states composed of such approximate GKP qubits as a coherent superposition of a Gaussian ensemble of randomly displaced ideal GKP-qubit graph states. Using standard Gaussian dynamics, we track the transformation of the covariance matrix and the mean-displacement vector elements of the Gaussian distribution of the ensemble under tools such as GKP-Steane error-correction and fusion operations that can be used to grow large high-fidelity GKP-qubit graph states. The covariance matrix elements capture the noise in the graph state due to the finite-energy approximation of GKP qubits, while the mean displacements relate to the possible absolute shift errors on the individual qubits arising conditionally from the homodyne measurements that are a part of these tools. Our work thus pins down an exact coherent error model for graph states generated from truly finite-energy GKP qubits, which can shed light on their error-correction properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1994

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Sheu, Bing J.↗

DOE OSTI2017

Development and Modeling of a Novel Self-Assembly Process for Polymer and Polymeric Composite Nanoparticles

Extensive computational simulations and experiments have been used to investigate the structure, dynamics and resulting photophysical properties of a number para-phenylenevinylene (PPV) based polymers and oligomers. These studies have shown how the morphology and structure are controlled to a large extent by the nature of the solute-solvent interactions in the initial solution phase preparation. A good solvent such as dichloromethane generates non-compact structures with more of a defect-extended chain like morphology while a bad solvent such as toluene leads to compact organized and folded structures with rod-like morphologies. Secondary structural organization is induced by using the solution phase structures to generate solvent-free single molecule nanoparticles. These nanoparticles are very compact and rod shaped, consisting of near-cofacial ordering of the conjugated PPV chain backbones between folds located at tetrahedral defects (sp3 C-C bonds). The resulting photophysical properties exhibit a significant enhancement in the photoluminescence quantum yield, lifetime, and stability. In addition, the single molecule nanoparticles have Gaussian-like emission spectra with discrete center frequencies that are correlated to a conjugation length, allowing the design of nanoparticles which luminesces at a particular frequency. We followed a similar approach and applied a comparable methodology in our recent work on polythiophenes in order to study the effect of polymer architecture on nanoscale assembly. Unlike linear chains of comparable size, we observed aggregation of the bottlebrush architecture of poly(norbornene)-g-poly(3-hexylthiophene) (PNB-g-P3HT) after the freeze-drying and dissolution processes. The behavior can be attributed to a significant enhancement in the number of π-π interactions between grafted P3HT side chains.

Sumpter, Bobby G.↗

DOE OSTI2021

Stochastic Inflation at NNLO

Stochastic Inflation is an important framework for understanding the physics of de Sitter space and the phenomenology of inflation. In the leading approximation, this approach results in a Fokker-Planck equation that calculates the probability distribution for a light scalar field as a function of time. Despite its successes, the quantum field theoretic origins and the range of validity for this equation have remained elusive, and establishing a formalism to systematically incorporate higher order effects has been an area of active study. In this paper, we calculate the next-to-next-to-leading order (NNLO) corrections to Stochastic Inflation using Soft de Sitter Effective Theory (SdSET). In this effective description, Stochastic Inflation manifests as the renormalization group evolution of composite operators. The leading impact of non-Gaussian quantum fluctuations appears at NNLO, which is presented here for the first time; we derive the coefficient of this term from a two-loop anomalous dimension calculation within SdSET. We solve the resulting equation to determine the NNLO equilibrium distribution and the low-lying relaxation eigenvalues. In the process, we must match the UV theory onto SdSET at one-loop order, which provides a non-trivial confirmation that the separation into Wilson-coefficient corrections and contributions to initial conditions persists beyond tree level. Furthermore, these results illustrate how the naive factorization of time and momentum integrals in SdSET no longer holds in the presence of logarithmic divergences. It is these effects that ultimately give rise to the renormalization group flow that yields Stochastic Inflation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗

NASA NTRS2013

Herschel Observations of the Debris Disc Around HIP 92043

Context: Typical debris discs are composed of particles ranging from several micron sized dust grains to kilometer-sized asteroidal bodies, and their infrared emission peaks at wavelengths 60-100 microns. Recent Herschel DUNES (Dust Around Nearby Stars) observations have identified several debris discs around nearby Sun-like stars (F, G and K spectral type) with significant excess emission only at 160 microns. Aims: We observed HIP 92043 (110 Her, HD 173667) at far-infrared and sub-millimetre wavelengths with Herschel PACS (Photodetector Array Camera and Spectrometer) and SPIRE (Spectral and Photometric Imaging Receiver).Identification of the presence of excess emission from HIP 92043 and the origin and physical properties of any excess was undertaken through analysis of its spectral energy distribution (SED) and the PACS images. Methods: The PACS and SPIRE images were produced using the HIPE (Herschel Interactive Processing Environment) photProject map maker routine. Fluxes were measured using aperture photometry. A stellar photosphere model was scaled to optical and near infrared photometry and subtracted from the far-infared and sub-mm fluxes to determine the presence of excess emission. Source radial profiles were fitted using a 2D Gaussian and compared to a PSF (Point Spread Function) model based on Herschel observations of alpha Boo to check for extended emission. Results: Clear excess emission from HIP 92043 was observed at 70 and 100 microns. Marginal excess was observed at 160 and 250 microns.Analysis of the images reveals that the source is extended at 160 microns. A fit to the source SED is inconsistent with a photosphere and single temperature black body. Conclusions: The excess emission from HIP 92043 is consistent with the presence of an unresolved circumstellar debris disc at 70and 100 microns, with low probability of background contamination. The extended 160 micron emission may be interpreted as an additional cold component to the debris disc or as the result of background contamination along the line of sight. The nature of the 160 micron excess cannot be determined absolutely from the available data, but we favour a debris disc interpretation, drawing parallels with previously identified cold disc sources in the DUNES sample.

debris discs↗

DOE OSTI2017

Processing Methodology for Double Sensor Probe in a Bubbly Flow

Bubbly flow phenomenon is the most common flow pattern in nuclear plant system. In this paper, according to Hibiki and Bartel, the experiment was designed to observe the fluid conditions surrounding nuclear rod. In recent years, double sensor probe is one of the measuring techniques for the bubbly flow. It can measure local void fraction and bubble velocity. After mathematical statistical calculation, interfacial area concentration is obtained by Kataoka. The double sensor probe method, with the advantage for measuring local interfacial area concentration, has become widely used in many measurement methods. The paper of Liu and Zhao were referred to establish the post-processing for the raw data obtained from measuring instrument. After drawing the signal of the probe, it is important to analyze the raw data for the elimination of unnecessary noise signal and highlight the characteristic signal. Based on the same experimental condition, the different filtering methods, Simple Moving Average (SMA) and Gaussian average, were compared and discussed by the double sensor probe.

42 ENGINEERING↗

DOE OSTI2021

An Ab Initio Multiple Cloning Method for Non-Adiabatic Excited-State Molecular Dynamics in NWChem

The recently developed ab-initio multiple cloning (AIMC) approach based on the multicongurational Ehrenfest (MCE) method provides a powerful and accurate way of describing the excited-state dynamics of molecular systems. The AIMC method is a controlled approximation to non-adiabatic dynamics with a particular strength in the proper description of decoherence effects due to branching of vibrational wavepackets at a level crossing. In this work, we report a new implementation of the AIMC algorithm in the open source NWChem computational chemistry program. The framework combines linear-response time-dependent density functional theory with Ehrenfest mean-eld theory to determine the equations of motion for classical trajectories. The multi-dimensional wave function is decomposed into a superposition of Gaussian coherent states guided by Ehrenfest trajectories (i.e., MCE approach), which can clone with fully quantum mechanical amplitudes and phases. By using an efficient time-derivative based non-adiabatic coupling approach within the AIMC method, all observable are calculated on-the-y in the non-adiabatic molecular dynamics process. As a representative example, we apply our implementation to study the ultrafast photoinduced electronic and vibrational energy transfer in a pyridine molecule. The effects of the cloning procedure on electronic and vibrational coherence, relaxation and unidirectional energy transfer are discussed. This new AIMC implementation provides a high-level non-adiabatic molecular dynamics framework for simulating photoexcited dynamics in complex molecular systems and experimentally relevant ultrafast spectroscopic probes, such as nonlinear coherent optical and X-ray signals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Highly Basic Clusters in the Herpes Simplex Virus 1 Nuclear Egress Complex Drive Membrane Budding by Inducing Lipid Ordering

During replication of herpesviruses, capsids escape from the nucleus into the cytoplasm by budding at the inner nuclear membrane. This unusual process is mediated by the viral nuclear egress complex (NEC) that deforms the membrane around the capsid by oligomerizing into a hexagonal, membrane-bound scaffold. Here, we found that highly basic membrane-proximal regions (MPRs) of the NEC alter lipid order by inserting into the lipid headgroups and promote negative Gaussian curvature. We also find that the electrostatic interactions between the MPRs and the membranes are essential for membrane deformation. One of the MPRs is phosphorylated by a viral kinase during infection, and the corresponding phosphomimicking mutations block capsid nuclear egress. We show that the same phosphomimicking mutations disrupt the NEC-membrane interactions and inhibit NEC-mediated budding in vitro, providing a biophysical explanation for the in vivo phenomenon. Our data suggest that the NEC generates negative membrane curvature by both lipid ordering and protein scaffolding and that phosphorylation acts as an off switch that inhibits the membrane-budding activity of the NEC to prevent capsid-less budding.

membrane curvature↗

NASA NTRS1989

Isolating contour information from arbitrary images

Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects.

Jobson, Daniel J.↗

NASA NTRS2004

Synoptic Study of an X-Ray Nova in Outburst (ADP 2000)

In the completed work listed above, we present an X--ray spectral and timing analysis of 4131543-47 during its 2002 outburst based on 49 pointed observations obtained using the Rossi X-ray Timing Explorer (RXTE). The outburst reached a peak intensity of 4.2 Crab in the 2-12 keV band and declined by a factor of 32 throughout the month-long observation. A 21.9+/-0.6 mJy radio flare was detected at 1026.75 MHz two days before the X-ray maximum; the radio source was also detected late in the outburst, after the X-ray source entered the hard state. The X-ray light curve exhibits the classic shape of a rapid rise and an exponential decay. The spectrum is soft and dominated by emission from the accretion disk. The continuum is fit with a multicolor disk blackbody (kT-max = 1.04 keV) and a power-law (Gamma -2.7). Midway through the decay phase, a strong low-frequency quasi-periodic oscillation (QPO; nu = 7.3-8.1 Hz) was present for several days. The spectra feature a broad Fe K line that is asymmetric, suggesting that the line is due to relativistic broadening rather than Gomptonization. Relativistic Laor models provide much better fits to the line than non-relativistic Gaussian models, particularly near the beginning and end of our observations. The line fits yield estimates for the inner disk radius that are within 6 GM/c 2; this result and additional evidence indicates that this black hole may have a non-zero angular momentum. In current work, we are in the process of analyzing a huge set of RXTE data (-100 pointed observations) for H1743-322, which was in eruption throughout most of 2003. The source s behavior is remarkable. It displays all of the canonical X-ray states, sometimes in unusual ways. In this short report, we feature a single important result, namely, the detection of a pair of high-frequency QPOs. The bright flaring behavior is of special interest. During the flares the shape of the power spectrum and the presence of low-frequency QPOs indicate that the source is in the steep power-law (SPL or very high) state. It is in this state that the high-frequency QPOs, which are believed to emanate from the very inner accretion disk, were oberved. Specifically, we observe a 240 Hz QPO at a significance of 4.7sigma and a 162 Hz QPO at 3.7sigma. The higher frequency QPO was discovered by Homan et al. in an independent data set; they also found marginal evidence (2.5sigma)

McClintock, Jeffrey↗

NASA NTRS1996

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Keel, Byron M.↗

NASA NTRS1999

Integration of a Decentralized Linear-Quadratic-Gaussian Control into GSFC's Universal 3-D Autonomous Formation Flying Algorithm

A decentralized control is investigated for applicability to the autonomous formation flying control algorithm developed by GSFC for the New Millenium Program Earth Observer-1 (EO-1) mission. This decentralized framework has the following characteristics: The approach is non-hierarchical, and coordination by a central supervisor is not required; Detected failures degrade the system performance gracefully; Each node in the decentralized network processes only its own measurement data, in parallel with the other nodes; Although the total computational burden over the entire network is greater than it would be for a single, centralized controller, fewer computations are required locally at each node; Requirements for data transmission between nodes are limited to only the dimension of the control vector, at the cost of maintaining a local additional data vector. The data vector compresses all past measurement history from all the nodes into a single vector of the dimension of the state; and The approach is optimal with respect to standard cost functions. The current approach is valid for linear time-invariant systems only. Similar to the GSFC formation flying algorithm, the extension to linear LQG time-varying systems requires that each node propagate its filter covariance forward (navigation) and controller Riccati matrix backward (guidance) at each time step. Extension of the GSFC algorithm to non-linear systems can also be accomplished via linearization about a reference trajectory in the standard fashion, or linearization about the current state estimate as with the extended Kalman filter. To investigate the feasibility of the decentralized integration with the GSFC algorithm, an existing centralized LQG design for a single spacecraft orbit control problem is adapted to the decentralized framework while using the GSFC algorithm's state transition matrices and framework. The existing GSFC design uses both reference trajectories of each spacecraft in formation and by appropriate choice of coordinates and simplified measurement modeling is formulated as a linear time-invariant system. Results for improvements to the GSFC algorithm and a multiple satellite formation will be addressed. The goal of this investigation is to progressively relax the assumptions that result in linear time-invariance, ultimately to the point of linearization of the non-linear dynamics about the current state estimate as in the extended Kalman filter. An assessment will then be made about the feasibility of the decentralized approach to the realistic formation flying application of the EO-1/Landsat 7 formation flying experiment.

Folta, David C.↗

DOE OSTI2019

Jet fragmentation transverse momentum measurements from di-hadron correlations in$\sqrt{\mathrm{s}}=7$ TeV pp and $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV p–Pb collisions

The transverse structure of jets was studied via jet fragmentation transverse momentum ( j T ) distributions, obtained using two-particle correlations in proton-proton and proton-lead collisions, measured with the ALICE experiment at the LHC. The highest transverse momentum particle in each event is used as the trigger particle and the region 3 < p T t < 15GeV/ c is explored in this study. The measured distributions show a clear narrow Gaussian component and a wide non-Gaussian one. Based on Pythia simulations, the narrow component can be related to non-perturbative hadronization and the wide component to quantum chromodynamical splitting. The width of the narrow component shows a weak dependence on the transverse momentum of the trigger particle, in agreement with the expectation of universality of the hadronization process. On the other hand, the width of the wide component shows a rising trend suggesting increased branching for higher transverse momentum. The results obtained in pp collisions at $\sqrt{\mathrm{s}}=7$ TeV and in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV are compatible within uncertainties and hence no significant cold nuclear matter effects are observed. The results are compared to previous measurements from CCOR and PHENIX as well as to Pythia 8 and Herwig 7 simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Inference of reaction rate parameters based on summary statistics from experiments

Here, we present the results of an application of Bayesian inference and maximum entropy methods for the estimation of the joint probability density for the Arrhenius rate para meters of the rate coefficient of the H 2 /O 2 -mechanism chain branching reaction H + O 2 → OH + O. Available published data is in the form of summary statistics in terms of nominal values and error bars of the rate coefficient of this reaction at a number of temperature values obtained from shock-tube experiments. Our approach relies on generating data, in this case OH concentration profiles, consistent with the given summary statistics, using Approximate Bayesian Computation methods and a Markov Chain Monte Carlo procedure. The approach permits the forward propagation of parametric uncertainty through the computational model in a manner that is consistent with the published statistics. A consensus joint posterior on the parameters is obtained by pooling the posterior parameter densities given each consistent data set. To expedite this process, we construct efficient surrogates for the OH concentration using a combination of Pad'e and polynomial approximants. These surrogate models adequately represent forward model observables and their dependence on input parameters and are computationally efficient to allow their use in the Bayesian inference procedure. We also utilize Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation resulting in orders of magnitude speedup in data likelihood evaluation. Despite the strong non-linearity in the model, the consistent data sets all res ult in nearly Gaussian conditional parameter probability density functions. The technique also accounts for nuisance parameters in the form of Arrhenius parameters of other rate coefficients with prescribed uncertainty. The resulting pooled parameter probability density function is propagated through stoichiometric hydrogen-air auto-ignition computations to illustrate the need to account for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

High field terahertz emission from relativistic laser-driven plasma wakefields

We propose a method to generate high field terahertz (THz) radiation with peak strength of GV/cm level in the THz frequency gap range of 1–10 THz using a relativistic laser interaction with a gaseous plasma target. Due to the effect of local pump depletion, an initially Gaussian laser pulse undergoes leading edge erosion and eventually evolves to a state with leading edge being step function. Interacting with such a pulse, electrons gain transverse residual momentum and excite net transverse currents modulated by the relativistic plasma frequency. These currents give rise to the low frequency THz emission. We demonstrate this process with one and two dimensional particle-in-cell simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Cross-beam energy transfer in direct-drive ICF. II. Theory and simulation of mitigation through increased laser bandwidth

The response of the cross-beam energy transfer instability (CBET) to laser bandwidth is investigated through a combination of theory and simulation. Existing linear theory is generalized to treat broadband lasers, demonstrating that CBET is most effectively suppressed when the bandwidth exceeds the ion-acoustic wave (IAW) frequency. It is shown that for such bandwidths, reverse (seed to pump) transfer becomes possible, which reduces the net energy transfer rapidly as bandwidth is increased. The CBET gain exponent in this regime scales with bandwidth (Δω) as Δω−3 for Gaussian or Lorentzian laser spectra with different scalings possible for other spectra. Comparison of our theory with linearized fluid and particle-in-cell simulations, performed with the laser-plasma simulation environment (LPSE) and vector particle in cell (VPIC) codes, respectively, finds that the model is accurate in the absence of nonlinear processes. However, linear analysis also finds that the IAW energy density scales as Δω−1, implying that nonlinear effects may be more difficult to control than the CBET scaling would suggest. Indeed, nonlinear effects are found to be present in VPIC simulations with high-intensity lasers, despite minimal apparent CBET. Nonlinear processes in the VPIC cases include particle trapping, the two-ion wave decay, and ion wave self-focusing. In some high intensity VPIC cases, these effects lead to net energy transfer from seed to pump and increases to backscatter stimulated Brillouin scattering reflectivities. Finally, for a given bandwidth, we show that improved control of nonlinear processes can be achieved via smoothing by spectral dispersion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗