Detecting Reconnection Sites Using the Lorentz Transformations for Electromagnetic Fields

Lapenta, Giovanni

doi:10.3847/1538-4357/abeb74

Cited by 12 publications

(10 citation statements)

References 63 publications

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“…We confirm this finding using a recently proposed indicator that is based on the properties of a class of Lorentz transformations that eliminate the local magnetic field (Lapenta 2021). We find that two populations of current clusters exist: one where agyrortropy, dissipation, and parallel electric field are all large and one where they are small.…”

Section: Introductionsupporting

confidence: 78%

Formation and Reconnection of Electron Scale Current Layers in the Turbulent Outflows of a Primary Reconnection Site

Lapenta

Goldman

Newman

et al. 2022

ApJ

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We simulate with 3D particle in cell, the spontaneous formation of turbulent outflows in an initially laminar 3D reconnecting current layer. We observe the formation of many secondary current layers and reconnection sites in the outflow. The approach we follow is to study each individual feature within the turbulent outflow. To identify all clusters of current in the outflow we use a clustering technique widely used in unsupervised machine learning: density-based spatial clustering of applications with noise. Once the clusters are identified we measure their size and compute reconnection indicators to establish which are undergoing reconnection. With this analysis we establish that the size of the current clusters reaches all the way from its initial system scale down to subelectron skin depth scale. We observe that the smaller current clusters are more prone to reconnecting and to releasing energy. We then find the process of reconnection of the smaller current cluster to be of the recently observed electron-only type that leaves the ions essentially unaffected.

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Section: Introductionsupporting

confidence: 78%

Formation and Reconnection of Electron Scale Current Layers in the Turbulent Outflows of a Primary Reconnection Site

Lapenta

Goldman

Newman

et al. 2022

ApJ

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“…(2019b), strong electron flow—acting as the current carrier—has been found inside the flux rope along the rope axis (see Figures 4a–4b in Fu et al. (2017)); in the previous simulation by Lapenta (2021) and observation by Chen et al. (2019b), electron crescent distribution was found inside the flux rope (O‐line) as well.…”

Section: Summary and Discussionmentioning

confidence: 67%

Subion‐Scale Flux Rope Nested Inside Ion‐Scale Flux Rope in Earth's Magnetotail

Liu

et al. 2021

Geophysical Research Letters

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Magnetic flux ropes (MFRs)—transient magnetic structures widely observed in Earth's magnetosphere—play crucial roles in particle acceleration and energy dissipation. MFRs above ion scale have been well studied, while MFRs at subion scales and their coupling with larger‐scale ropes still remains not well understood. In this paper, we present the first observation of a subion‐scale MFR nested in an ion‐scale MFR, using high‐resolution data from the magnetospheric multiscale (MMS) mission. We find that the subion‐scale MFR hosts more intense plasma activity than the ion‐scale MFR, including distinct electron agyrotropy and strong electromagnetic turbulence. In addition, axis of the subion‐scale MFR is oblique to that of the ion‐scale MFR, indicating that their generation may be ascribed to 3D magnetic reconnection. Observations of such cross‐scale structure may shed new lights into understanding energy cascade in space plasmas.

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“…A more recent discovery is that reconnection is associated with peculiar electron velocity distributions that present croissant-shaped features called crescents [541]. An especially convenient way to identify a possible reconnection site is the local measure of the so-called Lorentz indicator based on computing the speed of a frame transformation that eliminates the local magnetic field [542]. An example of this indicator is shown in Fig.…”

Section: F Magnetic Reconnectionmentioning

confidence: 99%

“…Kinetic simulation FIG. 28: The indicator defined in [542] identifies many reconnection sites visible in the picture as ghostly yellow-green areas. A group of electron flowlines are shown passing one of these reconnection sites and encountering also others.…”

Section: F Magnetic Reconnectionmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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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, plasmaprocessing 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. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Detecting Reconnection Sites Using the Lorentz Transformations for Electromagnetic Fields

Cited by 12 publications

References 63 publications

Formation and Reconnection of Electron Scale Current Layers in the Turbulent Outflows of a Primary Reconnection Site

Formation and Reconnection of Electron Scale Current Layers in the Turbulent Outflows of a Primary Reconnection Site

Subion‐Scale Flux Rope Nested Inside Ion‐Scale Flux Rope in Earth's Magnetotail

2022 Review of Data-Driven Plasma Science

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