Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques

Sisti, Manuela; Finelli, Francesco; Pedrazzi, Giorgio; Faganello, Matteo; Califano, Francesco; Ponti, Francesca Delli

doi:10.3847/1538-4357/abd24b

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…Turbulent plasmas also have the potential to generate complex 3D current structures. While the present analysis focuses on events that appear consistent with quasi-2D guide field reconnection over the scale they are traversed by the spacecraft, continued work should be done to explore more general methods of identifying more complex reconnection events in turbulent plasmas, as has begun to be explored in numerical simulations 69,140 . The examination of other types of current and magnetic field structures within the turbulent magnetosheath that may be associated with magnetic reconnection, in particular small-scale flux ropes that may be linked to the plasmoid instability, should be explored and will help to make contact with theories of how the disruption of current sheets by reconnection may influence the nonlinear dynamics of the turbulence.…”

Section: Discussionmentioning

confidence: 99%

Turbulence-driven magnetic reconnection and the magnetic correlation length: Observations from Magnetospheric Multiscale in Earth's magnetosheath

et al. 2022

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Turbulent plasmas generate a multitude of thin current structures that can be sites for magnetic reconnection. The Magnetospheric Multiscale (MMS) mission has recently enabled the detailed examination of such turbulent current structures in Earth's magnetosheath and revealed that a novel type of reconnection, known as electron-only reconnection, can occur. In electron-only reconnection, ions do not have enough space to couple to the newly reconnected magnetic fields, suppressing ion jet formation and resulting in thinner sub-proton-scale current structures with faster super-Alfvénic electron jets. In this study, MMS observations are used to examine how the magnetic correlation length (λ C ) of the turbulence, which characterizes the size of the largescale magnetic structures and constrains the length of the current sheets formed, influences the nature of turbulence-driven reconnection. We systematically identify 256 reconnection events across 60 intervals of magnetosheath turbulence. Most events do not appear to have ion jets; however, 18 events are identified with ion jets that are at least partially coupled to the reconnected magnetic field. The current sheet thickness and electron jet speed have a weak anti-correlation, with faster electron jets at thinner current sheets. When λ C < ∼ 20 ion inertial lengths, as is typical near the sub-solar magnetosheath, a tendency for thinner current sheets and potentially faster electron jets is present. The results are consistent with electron-only reconnection being more prevalent for turbulent plasmas with relatively short λ C , and may be relevant to the nonlinear dynamics and energy dissipation in turbulent plasmas.

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Section: Discussionmentioning

confidence: 99%

Turbulence-driven magnetic reconnection and the magnetic correlation length: Observations from Magnetospheric Multiscale in Earth's magnetosheath

et al. 2022

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“…DBSCAN has already been used successfully for a similar task of identifying currents where potential reconnection regions are in 2D simulations (Sisti et al 2021) and for clustering large numbers of particles in petascale 3D simulations (Patwary et al 2015). Moreover, DBSCAN is an algorithm widely used in countless machine-learning applications and is a core algorithm of most ML toolkits like the MATLAB ML toolkit or Sci-Kit in python.…”

Section: Methods Of Clustering Analysismentioning

confidence: 99%

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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“…Ref. [550] use DB-SCAN [551] and k-means [546] to identify current layers with a sufficiently large aspect ratio to flag reconnection.…”

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 Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques

Cited by 12 publications

References 33 publications

Turbulence-driven magnetic reconnection and the magnetic correlation length: Observations from Magnetospheric Multiscale in Earth's magnetosheath

Turbulence-driven magnetic reconnection and the magnetic correlation length: Observations from Magnetospheric Multiscale in Earth's magnetosheath

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

2022 Review of Data-Driven Plasma Science

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