Large amplitude plasma wakefields produced by a high power laser pulse in an underdense plasma were studied in a self-modulated laser wakefield accelerator (SM-LWFA) experiment. A pump-probe coherent ...Thomson scattering (CTS) technique was used and the lifetime of the wakefield was measured to be less than 5 psec. A plasma channel was observed to form in the wake of the pump laser pulse. The trailing probe laser pulse was observed to be guided by this channel for about 20 Rayleigh lengths. High energy electrons (up to 30 MeV) have been measured using a magnetic spectrometer. Highly non-linear plasma waves were also detected using forward Raman scattering diagnostics and were observed to correlate with the electron signals.
2022 Review of Data-Driven Plasma Science Anirudh, Rushil; Archibald, Rick; Asif, M. Salman ...
IEEE transactions on plasma science,
2023-July, 2023-7-00, 2023-07, 2023-07-01, Letnik:
51, Številka:
7
Journal Article
Recenzirano
Odprti dostop
Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven ...plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final Section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary Section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.
Data-driven methods (DDMs), such as deep neural networks, offer a generic
approach to integrated data analysis (IDA), integrated diagnostic-to-control
(IDC) workflows through data fusion (DF), which ...includes multi-instrument data
fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment
data fusion (SXDF). These features make DDMs attractive to nuclear fusion
energy and power plant applications, leveraging accelerated workflows through
machine learning and artificial intelligence. Here we describe Physics-informed
Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including
high-energy photons such as $\gamma$-rays from nuclear fusion), neutron and
others (such as proton radiography) measurements for nuclear fusion. PiMiX
solves multi-domain high-dimensional optimization problems and integrates
multi-modal measurements with multiphysics modeling through neural networks.
Super-resolution for neutron detection and energy resolved X-ray detection have
been demonstrated. Multi-modal measurements through MIDF can extract more
information than individual or uni-modal measurements alone. Further
optimization schemes through DF are possible towards empirical fusion scaling
laws discovery and new fusion reactor designs.
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma ...science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.
