Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data PDF Download
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Languages : en
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Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, time-varying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.
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Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, time-varying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.
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Languages : en
Pages : 25
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Numerical simulations of laser-plasma wakefield (particle) accelerators model the acceleration of electrons trapped in plasma oscillations (wakes) left behind when an intense laser pulse propagates through the plasma. The goal of these simulations is to better understand the process involved in plasma wake generation and how electrons are trapped and accelerated by the wake. Understanding of such accelerators, and their development, offer high accelerating gradients, potentially reducing size and cost of new accelerators. One operating regime of interest is where a trapped subset of electrons loads the wake and forms an isolated group of accelerated particles with low spread in momentum and position, desirable characteristics for many applications. The electrons trapped in the wake may be accelerated to high energies, the plasma gradient in the wake reaching up to a gigaelectronvolt per centimeter. High-energy electron accelerators power intense X-ray radiation to terahertz sources, and are used in many applications including medical radiotherapy and imaging. To extract information from the simulation about the quality of the beam, a typical approach is to examine plots of the entire dataset, visually determining the adequate parameters necessary to select a subset of particles, which is then further analyzed. This procedure requires laborious examination of massive data sets over many time steps using several plots, a routine that is unfeasible for large data collections. Demand for automated analysis is growing along with the volume and size of simulations. Current 2D LWFA simulation datasets are typically between 1GB and 100GB in size, but simulations in 3D are of the order of TBs. The increase in the number of datasets and dataset sizes leads to a need for automatic routines to recognize particle patterns as particle bunches (beam of electrons) for subsequent analysis. Because of the growth in dataset size, the application of machine learning techniques for scientific data mining is increasingly considered. In plasma simulations, Bagherjeiran et al. presented a comprehensive report on applying graph-based techniques for orbit classification. They used the KAM classifier to label points and components in single and multiple orbits. Love et al. conducted an image space analysis of coherent structures in plasma simulations. They used a number of segmentation and region-growing techniques to isolate regions of interest in orbit plots. Both approaches analyzed particle accelerator data, targeting the system dynamics in terms of particle orbits. However, they did not address particle dynamics as a function of time or inspected the behavior of bunches of particles. Ruebel et al. addressed the visual analysis of massive laser wakefield acceleration (LWFA) simulation data using interactive procedures to query the data. Sophisticated visualization tools were provided to inspect the data manually. Ruebel et al. have integrated these tools to the visualization and analysis system VisIt, in addition to utilizing efficient data management based on HDF5, H5Part, and the index/query tool FastBit. In Ruebel et al. proposed automatic beam path analysis using a suite of methods to classify particles in simulation data and to analyze their temporal evolution. To enable researchers to accurately define particle beams, the method computes a set of measures based on the path of particles relative to the distance of the particles to a beam. To achieve good performance, this framework uses an analysis pipeline designed to quickly reduce the amount of data that needs to be considered in the actual path distance computation. As part of this process, region-growing methods are utilized to detect particle bunches at single time steps. Efficient data reduction is essential to enable automated analysis of large data sets as described in the next section, where data reduction methods are steered to the particular requirements of our clustering analysis. Previously, we have described the application of a set of algorithms to automate the data analysis and classification of particle beams in the LWFA simulation data, identifying locations with high density of high energy particles. These algorithms detected high density locations (nodes) in each time step, i.e. maximum points on the particle distribution for only one spatial variable. Each node was correlated to a node in previous or later time steps by linking these nodes according to a pruned minimum spanning tree (PMST). We call the PMST representation 'a lifetime diagram', which is a graphical tool to show temporal information of high dense groups of particles in the longitudinal direction for the time series. Electron bunch compactness was described by another step of the processing, designed to partition each time step, using fuzzy clustering, into a fixed number of clusters.
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Languages : en
Pages : 42
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Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than hasbeen possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline.
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Languages : en
Pages : 6
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Particle accelerators enable scientists to study the fundamental structure of the universe, but have become the largest and most expensive of scientific instruments. In this project, we advanced the science and technology of laser-plasma accelerators, which are thousands of times smaller and less expensive than their conventional counterparts. In a laser-plasma accelerator, a powerful laser pulse exerts light pressure on an ionized gas, or plasma, thereby driving an electron density wave, which resembles the wake behind a boat. Electrostatic fields within this plasma wake reach tens of billions of volts per meter, fields far stronger than ordinary non-plasma matter (such as the matter that a conventional accelerator is made of) can withstand. Under the right conditions, stray electrons from the surrounding plasma become trapped within these "wake-fields", surf them, and acquire energy much faster than is possible in a conventional accelerator. Laser-plasma accelerators thus might herald a new generation of compact, low-cost accelerators for future particle physics, x-ray and medical research. In this project, we made two major advances in the science of laser-plasma accelerators. The first of these was to accelerate electrons beyond 1 gigaelectronvolt (1 GeV) for the first time. In experimental results reported in Nature Communications in 2013, about 1 billion electrons were captured from a tenuous plasma (about 1/100 of atmosphere density) and accelerated to 2 GeV within about one inch, while maintaining less than 5% energy spread, and spreading out less than 1/2 milliradian (i.e. 1/2 millimeter per meter of travel). Low energy spread and high beam collimation are important for applications of accelerators as coherent x-ray sources or particle colliders. This advance was made possible by exploiting unique properties of the Texas Petawatt Laser, a powerful laser at the University of Texas at Austin that produces pulses of 150 femtoseconds (1 femtosecond is 10-15 seconds) in duration and 150 Joules in energy (equivalent to the muzzle energy of a small pistol bullet). This duration was well matched to the natural electron density oscillation period of plasma of 1/100 atmospheric density, enabling efficient excitation of a plasma wake, while this energy was sufficient to drive a high-amplitude wake of the right shape to produce an energetic, collimated electron beam. Continuing research is aimed at increasing electron energy even further, increasing the number of electrons captured and accelerated, and developing applications of the compact, multi-GeV accelerator as a coherent, hard x-ray source for materials science, biomedical imaging and homeland security applications. The second major advance under this project was to develop new methods of visualizing the laser-driven plasma wake structures that underlie laser-plasma accelerators. Visualizing these structures is essential to understanding, optimizing and scaling laser-plasma accelerators. Yet prior to work under this project, computer simulations based on estimated initial conditions were the sole source of detailed knowledge of the complex, evolving internal structure of laser-driven plasma wakes. In this project we developed and demonstrated a suite of optical visualization methods based on well-known methods such as holography, streak cameras, and coherence tomography, but adapted to the ultrafast, light-speed, microscopic world of laser-driven plasma wakes. Our methods output images of laser-driven plasma structures in a single laser shot. We first reported snapshots of low-amplitude laser wakes in Nature Physics in 2006. We subsequently reported images of high-amplitude laser-driven plasma "bubbles", which are important for producing electron beams with low energy spread, in Physical Review Letters in 2010. More recently, we have figured out how to image laser-driven structures that change shape while propagating in a single laser shot. The latter techniques, which use t ...
Author: Raspberry Simpson
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Languages : en
Pages : 0
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Since the innovation of chirped pulse amplification by Donna Strickland and Gerard Morou in 1985, laser technology has evolved such that we can create short pulses of light (10−15 − 10−12 seconds) with high peak powers (1015 Watts) in small, focused spots (∼a few microns). A prolific area of research that has emerged over the last two decades is the use of these high-intensity lasers to drive particle beams. Possible applications of these particle sources include isotope production for medical applications, proton cancer therapy, and fusion energy schemes. This thesis focuses on laser-driven proton acceleration and adds to the existing foundation of work in the area by investigating new empirical relationships, conducting new measurements of the accelerating electric field responsible for laser-driven proton acceleration, and developing a new data analysis methodology using machine learning. This work first examines laser-driven proton acceleration in the multi-picosecond regime (>1ps) at laser intensities of 1017 - 1019 W/cm2. This is motivated by recent results on laser platforms like the National Ignition Facility-Advanced Radiographic Capability laser and the OMEGA-Extended Performance laser, which have demonstrated enhanced accelerated proton energies when compared to established scaling laws. A detailed scaling study was performed on the Titan laser, which provided the basis for a new analytical scaling presented in this thesis. In addition, high-repetition-rate (HRR) lasers that can operate at 1 Hz or faster are now coming online around the world, opening a myriad of opportunities for accelerating the rate of learning on laser-driven particle experiments. To unlock these applications, HRR diagnostics combined with real-time analysis tools must be developed to process experimental measurements and outputs at HRR. Towards this goal, this thes is presents a novel automated data analysis framework based on machine learning and proposes a new methodology based on representation learning to integrate heterogeneous data constrain parameters that are not directly measurable. Taken together, these thrusts enable a new preliminary framework for enhanced analysis of complex HRR experiments and a foundational step towards realizing the goal of tunable laser-driven particle sources.
Author: Yagang Zhang
Publisher: BoD – Books on Demand
ISBN: 9533070331
Category : Games & Activities
Languages : en
Pages : 448
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Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.
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Category : Aeronautics
Languages : en
Pages : 700
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Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author: Peicheng Yu
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Languages : en
Pages : 207
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Accelerators at the energy frontier have been the tool of choice for nearly a century for unraveling the structure of matter, space, and time. Today's accelerators are the most complex and expensive tools for scientific discovery built by humans. The capability of these accelerators has increased at an exponential rate due to the development of new accelerator concepts and technology. The capability of existing accelerator technology has plateaued, so that a future accelerator at the energy frontier will be so large and expensive that it is not clear it will be built. On the other hand, plasma based acceleration has emerged as a possible alternative technology with much recent progress in theory, simulation, and experiment. In plasma based acceleration intense short-pulse laser, or particle beam excites a plasma wave wakefield as it propagates through long regions of plasma. When a laser is used it is called laser wakefield acceleration (LWFA), and when a particle beam is used it is called plasma wakefield acceleration (PWFA). Simulations have contribute greatly to the recent progress by providing guidance and insight for existing experiments, and for permitting the study of parameters beyond the current reach of experiments. However, these simulations require much computing resources. Therefore, alternative numerical techniques are desired, and in some cases are needed. In this dissertation, we systematically explore the use of a simulation method for modeling LWFA using the particle-in-cell (PIC) method, called the Lorentz boosted frame technique. In the lab frame the plasma length is typically four orders of magnitude larger than the laser pulse length. Using this technique, simulations are performed in a Lorentz boosted frame in which the plasma length, which is Lorentz contracted, and the laser length, which is Lorentz expanded, are now comparable. This technique has the potential to reduce the computational needs of a LWFA simulation by more than four orders of magnitude, and is useful if there is no or negligible reflection of the laser in the lab frame. To realize the potential of Lorentz boosted frame simulations for LWFA, the first obstacle to overcome is a robust and violent numerical instability, called the Numerical Cerenkov Instability (NCI), that leads to unphysical energy exchange between relativistically drifting particles and their radiation. This leads to unphysical noise that dwarfs the real physical processes. In this dissertation, we first present a theoretical analysis of this instability, and show that the NCI comes from the unphysical coupling of the electromagnetic (EM) modes and Langmuir modes (both main and aliasing) of the relativistically drifting plasma. We then discuss the methods to eliminate them. In EM-PIC simulations of plasmas, Maxwell's equations are solved using a finite difference form for the derivatives in real space or using FFT's and solving the fields in wave number space. We show that the use of an FFT based solver has useful properties on the location and growth rate of the unstable NCI modes. We first show that the use of an FFT based solver permits the effective elimination of the NCI by both using a low pass filter in wave number space and by reducing the time step. We also show that because some NCI modes are very localized in wave number space, a modification of the numerical dispersion near these unstable modes can eliminate them. We next show that these strategies work just as well if the FFT is only used in the plasma drifting direction and propose a hybrid FFT/Finite Difference solver. This algorithm also includes a correction to the current from the standard charge conserving current deposit that ensures that Gauss's Law is satisfied for the FFT/Finite Difference divergence operator. However, the use of FFTs can lead to parallel scalability issues when there are many more cells along the drifting direction than in the transverse direction(s). We then describe an algorithm that has the potential to address this issue by using a higher order finite difference operator for the derivative in the plasma drifting direction, while using the standard second order operators in the transverse direction(s). The NCI for this algorithm is analyzed, and it is shown that the NCI can be eliminated using the same strategies that were used for the hybrid FFT/Finite Difference solver. This scheme also requires a current correction and filtering which require FFTs. However, we show that in this case the FFTs can be done locally on each parallel partition. We also describe how the use of the hybrid FFT/Finite Difference or the hybrid higher order finite difference/second order finite difference methods permit combining the Lorentz boosted frame simulation technique with another ``speed up'' technique, called the quasi-3D algorithm, to gain unprecedented speed up for the LWFA simulations. In the quasi-3D algorithm the fields and currents are defined on an $r-z$ PIC grid and expanded in azimuthal harmonics. The expansion is truncated with only a few modes so it has similar computational needs of a 2D $r-z$ PIC code. We show that NCI has similar properties in $r-z$ as in $z-x$ slab geometry and show that the same strategies for eliminating the NCI in Cartesian geometry can be effective for the quasi-3D algorithm leading to the possibility of unprecedented speed up. We also describe a new code called UPIC-EMMA that is based on fully spectral (FFT) solver. The new code includes implementation of a moving antenna that can launch lasers in the boosted frame. We also describe how the new hybrid algorithms were implemented into OSIRIS. Examples of LWFA using the boosted frame using both UPIC-EMMA and OSIRIS are given, including the comparisons against the lab frame results. We also describe how to efficiently obtain the boosted frame simulations data that are needed to generate the transformed lab frame data, as well as how to use a moving window in the boosted frame. The NCI is also a major issue for modeling relativistic shocks with PIC algorithm. In relativistic shock simulations two counter-propagating plasmas drifting at relativistic speeds are colliding against each other. We show that the strategies for eliminating the NCI developed in this dissertation are enabling such simulations being run for much longer simulation times, which should open a path for major advances in relativistic shock research.
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Languages : en
Pages : 14
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Recently there has been a great interest in laser-plasma accelerators as possible next-generation particle accelerators because of their potential for ultra high accelerating gradients and compact size compared with conventional accelerators. It is known that the laser pulse is capable of exciting a plasma wave propagating at a phase velocity close to the velocity of light by means of beating two-frequency lasers or an ultra short laser pulse. These schemes came to be known as the Beat Wave Accelerator (BWA) for beating lasers or as the Laser Wakefield Accelerator (LWFA) for a short pulse laser. In this paper, the principle of laser wakefield particle acceleration has been tested by the Nd:glass laser system providing a short pulse with a power of 10 TW and a duration of 1 ps. Electrons accelerated up to 18 MeV/c have been observed by injecting 1 MeV/c electrons emitted from a solid target by an intense laser impact. The accelerating field gradient of 30 GeV/m is inferred.
Author: Christopher Baird
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Languages : en
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