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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 : 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 :
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Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets.
Author: E. Wes Bethel
Publisher: CRC Press
ISBN: 1439875723
Category : Computers
Languages : en
Pages : 514
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Book Description
Visualization and analysis tools, techniques, and algorithms have undergone a rapid evolution in recent decades to accommodate explosive growth in data size and complexity and to exploit emerging multi- and many-core computational platforms. High Performance Visualization: Enabling Extreme-Scale Scientific Insight focuses on the subset of scientific visualization concerned with algorithm design, implementation, and optimization for use on today’s largest computational platforms. The book collects some of the most seminal work in the field, including algorithms and implementations running at the highest levels of concurrency and used by scientific researchers worldwide. After introducing the fundamental concepts of parallel visualization, the book explores approaches to accelerate visualization and analysis operations on high performance computing platforms. Looking to the future and anticipating changes to computational platforms in the transition from the petascale to exascale regime, it presents the main research challenges and describes several contemporary, high performance visualization implementations. Reflecting major concepts in high performance visualization, this book unifies a large and diverse body of computer science research, development, and practical applications. It describes the state of the art at the intersection of scientific visualization, large data, and high performance computing trends, giving readers the foundation to apply the concepts and carry out future research in this area.
Author: Yagang Zhang
Publisher: BoD – Books on Demand
ISBN: 9533070331
Category : Games & Activities
Languages : en
Pages : 448
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Book Description
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.
Author: Weizhong Qiang
Publisher: Springer
ISBN: 3319284304
Category : Computers
Languages : en
Pages : 409
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Book Description
This book constitutes the refereed proceedings of the Second International Conference on Cloud Computing and Big Data, CloudCom-Asia 2015, held in Huangshan, China, in June 2015. The 29 full papers and two keynote speeches were carefully reviewed and selected from 106 submissions. The papers are organized in topical sections on cloud architecture; applications; big data and social network; security and privacy.
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Languages : en
Pages :
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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 : 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: Gerard Mourou
Publisher:
ISBN: 9811217130
Category : Electronic books
Languages : en
Pages : 269
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Author: Raspberry Simpson
Publisher:
ISBN:
Category :
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.
