arXiv subjects

Giovanni Lapenta

Publications and source records attributed to Giovanni Lapenta.

At least 37 records · Page 2Linked to original sources

arXiv2021

Bridging hybrid- and full-kinetic models with Landau-fluid electrons: I. 2D magnetic reconnection

Magnetic reconnection (MR) plays a fundamental role in plasma dynamics under many different conditions, from space and astrophysical environments to laboratory devices. High-resolution in-situ measurements from space missions allow to study naturally occurring MR processes in great detail. Alongside direct measurements, numerical simulations play a key role in investigating the fundamental physics underlying MR. The choice of an adequate plasma model to be employed in numerical simulations, while also compromising with their computational cost, is crucial to efficiently address the problem. We consider a new plasma model that includes a refined electron response within the hybrid-kinetic framework (kinetic ions, fluid electrons). The extent to which this new model can reproduce a full-kinetic description of 2D MR, with particular focus on its robustness during the non-linear stage, is evaluated. We perform 2D simulations of MR with moderate guide field by means of three different plasma models: a hybrid-Vlasov-Maxwell model with isotropic, isothermal electrons, a hybrid-Vlasov-Landau-fluid (HVLF) model where an anisotropic electron fluid is equipped with a Landau-fluid closure, and a full-kinetic one. When compared to the full-kinetic case, the HVLF model effectively reproduces the main features of MR, as well as several aspects of the associated electron micro-physics and its feedback onto proton dynamics. This includes the global evolution of MR and the local physics occurring within the so-called electron-diffusion region, as well as the evolution of species pressure anisotropy. In particular, anisotropy driven instabilities (such as firehose, mirror, and cyclotron instabilities) play a relevant role in regulating electrons anisotropy during the non-linear stage of MR. As expected, the HVLF model captures all these features, except for the electron-cyclotron instability.

physics.plasm-ph↗

arXiv2021

Multi-Beam Energy Moments of Compound Measured Ion Velocity Distributions

Compound ion distributions, fi(v), have been measured by NASA's Magnetospheric Multi-Scale Mission (MMS) and have been found in reconnection simulations. A complex distribution, fi(v), consisting, for example, of essentially disjoint pieces will be called a multi-beam distribution and modeled as a sum of "beams," fi(v) = f1(v) + ... +fN(v). Velocity moments of fi(v) are taken beam by beam and summed. Such multi-beam moments of fi(v) have advantages over the customary standard velocity moments of fi(v), forwhich there is only one mean flow velocity. For example, the standard thermal energy momentof a pair of equal and opposite cold particle beams is non-zero even though each beam has zero thermal energy. We therefore call this thermal energy pseudo-thermal. By contrast, a multi-beam moment of two or more beams has no pseudo-thermal energy. We develop three different ways of decomposing into a sum and finding multi-beam moments for both a multi-beam fi(v) measured by MMS in the dayside magnetosphere during reconnection and a multi-beam fi(v) found in a PIC simulation of magnetotail reconnection. The three methods are: A visual method in which the velocity centroid of each beam is estimated and its density determined self-consistently; A k-means method in which particles in a particle-representation of fi(v) are sorted into a minimum energy configuration of N (= k) clusters; A nonlinear least squares method based on a fit to a sum of N kappa functions. Multi-beam energy moments are calculated and compared with standard moments for the thermal energy density, pressure tensor, thermal energy flux (heat plus enthalpy fluxes), bulk kinetic energy density, RAM pressure and bulk kinetic energy flux. Applying this new formalism to real data demonstrates in detail how multi-beam techniques may provide significant insight into the properties of observed space plasmas.

physics.plasm-ph↗

arXiv2021

Detecting reconnection sites using the Lorentz Transformations for electromagnetic fields

We take a pragmatic definition of reconnection to find locations where a reconnection electric field causes a ExB drift that carries two components of the magnetic field towards their elimination. With this in mind as our target, we observe that such locations can be found using a new indicator: the velocity of the Lorentz transformation that eliminates two components of the local magnetic field. Serendipitously, the indicator naturally becomes subluminal in the close proximity of a point where two components of the magnetic field vanish and it is hard zero at the vanishing location. Everywhere else the velocity of this Lorentz frame change far exceeds the speed of light. This property can be quickly applied in practice because computing the frame change is a local operation that requires only the knowledge of the local magnetic and electric field: it can be applied in a simulation or in observational data from a field instrument. We further show that the points identified can be classified in 6 categories that extend the usual types of magnetic nulls to the case of 3D reconnection in presence of a guide field. The approach is used to identify secondary electron scale reconnection sites in a turbulent outflow from a primary reconnection site in a highly resolved massively parallel fully kinetic particle in cell simulation. Numerous points are found and their detailed analysis is reported.

astro-ph.SR↗

arXiv2020

Kinetic Modeling of Magnetospheres

This paper presents the state of the art of kinetic modeling techniques for simulating plasma kinetic dynamics in magnetospheres. We describe the critical numerical techniques for enabling large-scale kinetic simulations of magnetospheres: parameter scaling, implicit Particle-in-Cell schemes, and fluid-kinetic coupling. We show an application of these techniques to study particle acceleration and heating in asymmetric magnetic reconnection in the Ganymede magnetosphere.

physics.space-ph↗

arXiv2020

Turbulent energization of electron power law tails during magnetic reconnection

Earth's magnetotail is an excellent laboratory to study the interplay of reconnection and turbulence in determining electron energization. The process of formation of a power law tail during turbulent reconnection is a documented fact still in need of a comprehensive explanation. We conduct a massively parallel particle in cell 3D simulation and use enhanced statistical resolution of the high energy range of the particle velocities to study how reconnection creates the conditions for the tail to be formed. The process is not direct acceleration by the coherent, laminar, reconnection-generated electric field. Rather, reconnection causes turbulent outflows where energy exchange is dominated by a highly non-gaussian distribution of fluctuations. Electron energization is diffuse throughout the entire reconnection outflow but it is heightened by regions of intensified magnetic field such as dipolarization fronts traveling towards Earth.

physics.plasm-ph↗

arXiv2020

Domain of Influence analysis: implications for Data Assimilation in space weather forecasting

Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of \textit{Representer analysis (RA)} and \textit{Domain of Influence (DOI) analysis} to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind - magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps towards the ambitious goal of identifying the most relevant heliospheric parameters for modelling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.

physics.space-ph↗

arXiv2020

Automatic classification of plasma regions in near-Earth space with supervised machine learning: application to Magnetospheric Multi Scale 2016-2019 observations

The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic reconnection, waves and turbulence, jets and their combinations. The majority of available studies have been performed by using human-driven methods, such as visual data selection or the application of predefined thresholds to different observable plasma quantities. While human-driven methods have allowed performing many statistical studies, these methods are often time-consuming and can introduce important biases. On the other hand, the recent availability of large, high-quality spacecraft databases, together with major advances in machine-learning algorithms, can now allow meaningful applications of machine learning to in-situ plasma data. In this study, we apply the fully convolutional neural network (FCN) deep machine-leaning algorithm to the recent Magnetospheric Multi Scale (MMS) mission data in order to classify ten key plasma regions in near-Earth space for the period 2016-2019. For this purpose, we use available intervals of time series for each such plasma region, which were labeled by using human-driven selective downlink applied to MMS burst data. We discuss several quantitative parameters to assess the accuracy of both methods. Our results indicate that the FCN method is reliable to accurately classify labeled time series data since it takes into account the dynamical features of the plasma data in each region. We also present good accuracy of the FCN method when applied to unlabeled MMS data. Finally, we show how this method used on MMS data can be extended to data from the Cluster mission, indicating that such method can be successfully applied to any in situ spacecraft plasma database.

physics.plasm-ph↗

arXiv2020

Statistical Properties of Turbulent Fluctuations Associated with Electron-only Magnetic Reconnection

Context: Recent satellite measurements in the turbulent magnetosheath of Earth have given evidence of an unusual reconnection mechanism that is driven exclusively by electrons. This newly observed process was called electron-only reconnection, and its inter-play with plasma turbulence is a matter of great debate. Aims: By using 2D-3V hybrid Vlasov-Maxwell simulations of freely decaying plasma turbulence, we study the role of electron-only reconnection in the development of plasma turbulence. In particular, we search for possible differences with respect to the turbulence associated with standard ion-coupled reconnection. Methods: We analyzed the structure functions of the turbulent magnetic field and ion fluid velocity fluctuations to characterize the structure and the intermittency properties of the turbulent energy cascade. Results: We find that the statistical properties of turbulent fluctuations associated with electron-only reconnection are consistent with those of turbulent fluctuations associated with standard ion-coupled reconnection, and no peculiar signature related to electron-only reconnection is found in the turbulence statistics. This result suggests that the turbulent energy cascade in a collisionless magnetized plasma does not depend on the specific mechanism associated with magnetic reconnection. The properties of the dissipation range are discussed as well, and we claim that only electrons contribute to the dissipation of magnetic field energy at sub-ion scales.

physics.plasm-ph↗

arXiv2020

Visualizing and Interpreting Unsupervised Solar Wind Classifications

One of the goals of machine learning is to eliminate tedious and arduous repetitive work. The manual and semi-automatic classification of millions of hours of solar wind data from multiple missions can be replaced by automatic algorithms that can discover, in mountains of multi-dimensional data, the real differences in the solar wind properties. In this paper we present how unsupervised clustering techniques can be used to segregate different types of solar wind. We propose the use of advanced data reduction methods to pre-process the data, and we introduce the use of Self-Organizing Maps to visualize and interpret 14 years of ACE data. Finally, we show how these techniques can potentially be used to uncover hidden information, and how they compare with previous manual and automatic categorizations.

physics.space-ph↗

arXiv2020

Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning

Models based on neural networks and machine learning are seeing a rise in popularity in space physics. In particular, the forecasting of geomagnetic indices with neural network models is becoming a popular field of study. These models are evaluated with metrics such as the root-mean-square error (RMSE) and Pearson correlation coefficient. However, these classical metrics sometimes fail to capture crucial behavior. To show where the classical metrics are lacking, we trained a neural network, using a long short-term memory network, to make a forecast of the disturbance storm time index at origin time $t$ with a forecasting horizon of 1 up to 6 hours, trained on OMNIWeb data. Inspection of the model's results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications. However, visual inspection showed that the predictions made by the neural network were behaving similarly to the persistence model. In this work, a new method is proposed to measure whether two time series are shifted in time with respect to each other, such as the persistence model output versus the observation. The new measure, based on Dynamical Time Warping, is capable of identifying results made by the persistence model and shows promising results in confirming the visual observations of the neural network's output. Finally, different methodologies for training the neural network are explored in order to remove the persistence behavior from the results.

cs.LG↗

arXiv2020

Tokamak disruption prediction using different machine learning techniques

Disruption prediction and mitigation is of key importance in the development of sustainable tokamakreactors. Machine learning has become a key tool in this endeavour. In this paper multiple machinelearning models will be tested and compared. A particular focus has been placed on their portability.This describes how easily the models can be used with data from new devices. The methods used inthis paper are support vector machine, 2-tiered support vector machine, random forest, gradient boostedtrees and long-short term memory. The results show that the support vector machine performanceis marginally better among the standard models, while the gradient boosted trees performed the worst.The portable variant of each model had lower performance. Random forest obtained the highest portableperformance. Results also suggest that disruptions can be detected as early as 600ms before the event.An analysis of the computational cost showed all models run in less than 1ms, allowing sufficient timefor disruption mitigation.

physics.plasm-ph↗

arXiv2019

Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions

We present a method based on unsupervised machine learning to identify regions of interest using particle velocity distributions as a signature pattern. An automatic density estimation technique is applied to particle distributions provided by PIC simulations to study magnetic reconnection. The key components of the method involve: i) a Gaussian mixture model determining the presence of a given number of subpopulations within an overall population, and ii) a model selection technique with Bayesian Information Criterion to estimate the appropriate number of subpopulations. Thus, this method identifies automatically the presence of complex distributions, such as beams or other non-Maxwellian features, and can be used as a detection algorithm able to identify reconnection regions. The approach is demonstrated for specific double Harris sheet simulations but it can in principle be applied to any other type of simulation and observational data on the particle distribution function.

physics.plasm-ph↗

arXiv2019

Statistical analysis of ions in two-dimensional plasma turbulence

The statistical properties of ions in two-dimensional fully developed turbulence have been compared between two different numerical algorithms. In particular, we compare Hybrid Particle In Cell (hybrid PIC with fluid electrons) and full PIC simulations, focusing on particle diffusion and acceleration phenomena. To investigate several heliospheric plasma conditions, a series of numerical simulations has been performed by varying the plasma $β$ - the ratio between kinetic and magnetic pressure. These numerical studies allow the exploration of different scenarios, going from the solar corona (low $β$) to the solar wind ($β\sim 1$), as well as the Earth's magnetosheath (high $β$). It has been found that the two approaches compare pretty well, especially for the spectral properties of the magnetic field and the ion diffusion statistics. Small differences among the models have been found regarding the electric field behaviour at sub-ion scales and the acceleration statistics, due evidently to the more consistent treatment of the plasma in the full PIC approach.

physics.space-ph↗

arXiv2019

A comprehensive comparison of relativistic particle integrators

We compare relativistic particle integrators commonly used in plasma physics showing several test cases relevant for astrophysics. Three explicit particle pushers are considered, namely the Boris, Vay, and Higuera-Cary schemes. We also present a new relativistic fully implicit particle integrator that is energy conserving. Furthermore, a method based on the relativistic guiding center approximation is included. The algorithms are described such that they can be readily implemented in magnetohydrodynamics codes or Particle-in-Cell codes. Our comparison focuses on the strengths and key features of the particle integrators. We test the conservation of invariants of motion, and the accuracy of particle drift dynamics in highly relativistic, mildly relativistic, and non-relativistic settings. The methods are compared in idealized test cases, i.e., without considering feedback on the electrodynamic fields, collisions, pair creation, or radiation. The test cases include uniform electric and magnetic fields, $\mathbf{E}\times\mathbf{B}$-fields, force-free fields, and setups relevant for high-energy astrophysics, e.g., a magnetic mirror, a magnetic dipole, and a magnetic null. These tests have direct relevance for particle acceleration in shocks and in magnetic reconnection.

astro-ph.IM↗

arXiv2019

Discovery of an Electron Gyroradius Scale Current Layer Its Relevance to Magnetic Fusion Energy, Earth Magnetosphere and Sunspots

In the Earth magnetosphere, sunspots and magnetic cusp fusion devices, the boundary between the plasma and the magnetic field is marked by a diamagnetic current layer with a rapid change in plasma pressure and magnetic field strength. First principles numerical simulations were conducted to investigate this boundary layer with a spatial resolution beyond electron gyroradius while incorporating a global equilibrium structure. The boundary layer thickness is discovered to be on the order of electron gyroradius scale due to a self-consistent electric field suppressing ion gyromotion at the boundary. Formed at the scale of the electron gyroradius, the electric field plays a critical role in determining equilibrium structure and plasma transport. The discovery highlights the necessity to incorporate electron gyroradius scale physics in studies aimed at advancing our understanding of fusion devices, the magnetosphere and sunspots.

physics.plasm-ph↗

arXiv2018

First Detection of Solar Flare Emission in Middle-Ultraviolet Balmer Continuum

We present the first detection of solar flare emission at middle-ultraviolet wavelengths around 2000 A by the channel 2 of the Large-Yield RAdiometer (LYRA) onboard the PROBA2 mission. The flare (SOL20170906) was also observed in the channel 1 of LYRA centered at the H I Lyman-α line at 1216 A, showing a clear non-thermal profile in both channels. The flare radiation in channel 2 is consistent with the hydrogen Balmer continuum emission produced by an optically thin chromospheric slab heated up to 10000 K. Simultaneous observations in channels 1 and 2 allow the separation of the line emission (primarily from the Lyman-α line) from the Balmer continuum emission. Together with the recent detection of the Balmer continuum emission in the near-ultraviolet by IRIS, the LYRA observations strengthen the interpretation of broadband flare emission as the hydrogen recombination continua originating in the chromosphere.

astro-ph.SR↗

arXiv2018

ECsim-CYL: Energy Conserving Semi-Implicit particle in cell simulation in axially symmetric cylindrical coordinates

Based on the previously developed Energy Conserving Semi Implicit Method (ECsim) code, we present its cylindrical implementation, called ECsim-CYL, to be used for axially symmetric problems. The main motivation for the development of the cylindrical version is to greatly improve the computational speed by utilizing cylindrical symmetry. The ECsim-CYL discretizes the field equations in two-dimensional cylindrical coordinates using the finite volume method . For the particle mover, it uses a modification of ECsim's mover for cylindrical coordinates by keeping track of all three components of velocity vectors, while only keeping radial and axial coordinates of particle positions. In this paper, we describe the details of the algorithm used in the ECsim-CYL and present a series of tests to validate the accuracy of the code including a wave spectrum in a homogeneous plasmas inside a cylindrical waveguide and free expansion of a spherical plasma ball in vacuum. The ECsim-CYL retains the stability properties of ECsim and conserves the energy within machine precision, while accurately describing the plasma behavior in the test cases.

physics.comp-ph↗

arXiv2018

Implicit temporal discretization and exact energy conservation for particle methods applied to the Poisson-Boltzmann equation

Wereportonanewmultiscalemethodapproachforthestudyofsystemswith wide separation of short-range forces acting on short time scales and long-range forces acting on much slower scales. We consider the case of the Poisson-Boltzmann equation that describes the long-range forces using the Boltzmann formula (i.e. we assume the medium to be in quasi local thermal equilibrium). We developed a new approach where fields and particle information (mediated by the equations for their moments) are solved self-consistently. The new approach is implicit and numerically stable, providing exact energy conservation. We tested different implementations all leading to exact energy conservation. The new method requires the solution of a large set of non-linear equations. We considered three solution strategies: Jacobian Free Newton Krylov, an alternative, called field hiding, based on hiding part of the residual calculation and replacing them with direct solutions and a Direct Newton Schwarz solver that considers simplified single particle-based Jacobian. The field hiding strategy proves to be the most efficient approach.

physics.comp-ph↗