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Artificial Intelligence Data Analysis

Deliverables

Online report on the management activity

All management activities eg organization of events actions taken to promote the progress of AIDA will be documented online A web site will be created for the project to document all activities

Minutes of the 1st Advisory Board meeting

Minutes of the 1st Advisory Board meeting

Final report of WP1: documentation of awareness and wider societal implications

We will produce a report on the management practices with respect to our awareness to wider societal implications AIDA does not foresee any genderracialsocietal issues per se But space science has a great potential for societal relevance scientific outreach industrial expansion in space and human exploration and the management team will remain vigilant that all opportunities are taken to maximize the societal benefit of AIDA Additionally Artificial Intelligence aspects treated by AIDA of the project are relevant to vast areas of human activities well beyond space Image recognition used for space imagery can be applied to other fields sometimes controversially We will address this aspect in our project Avoiding any discriminatory practices and a gender balance will be our goal during the management and in hiring practices

Report on extreme event identification

In this deliverable a software that identifies anomalous and extreme events in spacecraft observations will be produced This module of the AIDApy will make use of highorder statistical techniques such as intermittency measurements and discontinuity identification techniques The design of this module consists of a treelike structure applying first a thresholdbased technique and then a local study of the parameters and the geometry of each event The algorithm will also make use of a newly developed technique known as Partial Variance of Increments PVI applied here to all the fluctuating quantities This AIDA module will lead to the identification of large amount of structures such as CMEs shocks fluidlike vortices discontinuities reconnection events as well as large amplitude waves The analysis will be applied to to existing and upcoming space missions and the outcome will consist in high level data products

Report on the interface of AIDA and external databases

It will be reported how the AIDApp will be combined into a Python package AIDADdb which will interface with the AIDA database at CINECA The AIDAdb package will export both the data downloaded by Tool 1 and the higher level products generated by Tool 2 eg lists of events plots etc into the AIDA DB The tool to export dataproducts into the AIDA DB can be the same as Tool 1 or a modified version of it The interface between the AIDAdb package and the AIDA database will be defined in an Interface Control Document ICD If times and resources will be sufficient the package will also be interfaced with external databases eg ESA or NASA databases existing Virtual Observatories etc

Exploitation Plan

This report will present the steps that the consortium will follow to obtain a lasting impact in the scientific community and the European industry This plan will lay out the strategies to spread the final outcomes of the project AIDA and its two main products the python package AIDApy and the database AIDAdb

Minutes of the 3rd Advisory Board meeting

Minutes of the 3rd Advisory Board meeting

Report on code and simulations selected for inclusion in AIDAdb

The report, in collaboration with Cineca WP10, will be written at the end of Task 2. It will focus first on the “low-level data”, i.e. the numerical simulations of interest and available to the project, and on their storage on the AIDdb. The second part of the report, in collaboration with WP4, will focus on the “high-level data”, i.e. the data obtained by exploiting the simulation data.

Report on the identification of numerical simulations to analyze and on the collection of data by virtual spacecraft

Once WP7 finalized the selection of numerical simulation data for inclusion in AIDAdb (deliverable D7.2), we will produce a report on the identification of numerical data suitable to be analyzed through virtual spacecraft techniques. Existing numerical runs will be selected to cover different regions of interests and different specific physical processes, e. g. particle heating, reconnection exhaust, magnetopause crossing, etc. Through these numerical simulations, we will provide virtual spacecraft measurements, which will be employed to support the design and training phase of novel techniques of artificial intelligence for data analysis and interpretation (AIDApp), aiming at identifying regions of scientific interest, based on time series of fields and particle velocity distributions. If needed, we will also plan and design new numerical simulations, in collaboration with AIDApp Data Assimilation and Analysis Tools (WP4 and WP6), able to simulate realistic conditions of the heliosphere environment.

Report on Python tool to perform human-driven selection

The report will present the results of the adaptation of currently available MATLAB tools to perform humandriven data selection to Phyton environment A dedicated AIDA Phyton tool will be generated Tool 2 of AIDApp The report will also address the issue if this dedicated tool can be integrated into existing Phyton packages such as spacepy sunpy astropy PlasmaPy and the AIDA project can integrate into these projects

Report on Analysis and selection of the software framework

This report will show the performances of different Neural Networks using different AI frameworks. We will perform a selection of benchmark cases, including multiple types of NN and multiple topologies. This report present the results from the analysis of multiple AI frameworks. Possible software includes: Keras, Torch, TensorFlow, Theano, etc. The goal of this report is to select a single AI framework to be used in the AIDApp package.

Report on Python tool to download data from open-access archives

The report will present the analysis of different available Phyton tool (spacepy,sunpy,astropy, PlasmaPy) and of other tools (irfu-matlab, SPEDAS) in order to decide by M6 which Phyton tool will be used as tool to download in situ, remote and ground plasma data from open-access archives (Tool 1 of AIDApp). Namely, if AIDA will use existing available Phyton tools, adapt available MATLAB or IDL tools to Phyton or a combination of both approaches.

Report on the use of AI techniques for analysis of heliospheric data

We intend to apply and train the ML software developed by WP2 WP3 WP9 to individuate or exclude the occurrence of basic phenomena of interest eg reconnection coherent structures at a given scale plasma heating and particle acceleration Selecting different data sets of similar physical configurations but evolving in a different way will give us the possibility to check if the Machine Learning algorithm is able to identify the real occurrence of a phenomenon or not Furthermore the virtual satellite technique will allow us to generate a large quantity of data needed to train ML algorithms since an infinite number of crossing in a simulation is possible The final goal will be to automatically identify in a large set of data subsets of much smaller size selected by the ML algorithms that contain events of strong scientific interest and impact for the whole space community Once the robustness of ML performing such tasks is proven we intend to apply them to the selected spacecraft data in different regions of interest eg the solar wind the magnetosheath in order to make direct comparison with numerical simulations

Report on application of DA techniques to OpenGGCM

The report at the end of Task 1 will detail initial activities performed with the aim of developing as part of the AIDA Python package tools to assimilate observational data into code and to couple it with existing codes. We will use in particular the example of c s\introducing data assimilation techniques in the global magnetospheric code OpenGGCM. The initial model sensitivity study will provide information on the temporal evolution and spatial structure of the ensemble variance, on the sensitivity of the model to the variation of the model inputs, and on the domain of influence of observations. This last analysis in particular, which can be performed before implementing observation assimilation by analysing the ensemble correlation between state variables at different grid points, will provide guidelines on the variables and locations which should be privileged for assimilation during the rest of the work. The report will also describe the infrastructure developed for the assimilation of observations into OpenGGCM.

Exploitation plan version 1

First version of the exploitation plan of the outcomes after the end of the project.

Deliverable progress report

Report on the progress of all the different deliverables of the project

Report on wave identification tool and Vlasov solver platform

We will deliver a new Python tool aimed at the statistical study of waves together with an opensource algorithm for solving the linear Vlasov system of equations Multipoint measurements allowed in space missions such as CLUSTER and MMS enable spatiotemporal effects to be resolved One application of these measurements is the determination of the wavevectors and hence the identification of wave modes that exist within the plasma This part of the AIDApy will employ two methods known as the phase differencing technique and the kfiltering The results will be directly interpreted in terms of plasma dispersion relations

Report on the statistical methods included in AIDApp

We will produce an open source code for the statistical analysis of variables, dedicated to in situ space missions. Unifying several analysis methods, we will provide a complete suite of data analysis tools for heliophysics. This major deliverable consists in building an integrated software library that accumulates analysis methods able to reveal the structure and topology of the fluctuating fields, such as the velocity, the density and the magnetic field of the interplanetary plasma. The library will be versatile enough to ingest data from various missions, thanks to the interaction with the other WPs. The toolbox will apply a full package of analysis methods, achieving the following three major products: low-order statistics, high-order statistics, and graphics. The low-order statistics will consist of a Python software aimed to the characterization of the large scale profiles (averages, averaged-profiles and variances). The high-order statistics deliverable will be dedicated to the power spectra and structure function analysis. Finally, an open source graphical user interface, as MATPLOTLIB and gnuplot, will be produced for AIDApp.

Report on data clustering

We will report on our results concerning the unsupervised classification We will apply clustering algorithms to datasets spanning long time periods derived from constellations of satellites orbiting in similar regions eg solar wind magnetopause magnetosphere etc in order to define dynamical states When different clusters are easily discernible we will examine the properties of such clusters via traditional statistical method WP4 In turn this will produce an automatic way of distinguishing between uneventful and eventful states for each different heliospheric region Moreover by applying clustering to previously identified latent variableswe will possibly discover new rules to detect anomalous states

Identifying magnetospheric regions from simulation and observational data with Machine Learning algorithms

The report describes the application of ML techniques developed within other AIDA work packages to the terrestrial magnetosphereWe aim at identifying plasma regions and events in the terrestrial magnetosphere using classification methods unsupervised and supervised When simulations are used we simulate the system with the 3DMagnetoHydroDynamic MHD code OpenGGCM We first describe the training and validation of a Self Organizing Map from simulation data This classification experiment aims at identifying large scale magnetospheric regions and it is tested on virtual satellite data obtained from the simulationThis method could be used in spacecraft data to trigger burst mode when region crossing is detectedThen we describe a supervised classification of MMS data aimed at identifying specific structures Dipolarizing Flux Bundles

Classification: catalog of reconnection events and boundary layers

In collaboration with WPs 57 8 we will 1 create an accessible catalog of reconnection and boundary layers events that can be easily analyzed by space physicists the catalog will eventually be released with the AIDAdb 2 release the trained algorithm that allows to classify new events to be used on unseen data such as new missions

Report on energetic particle analysis

The deliverable will consist of a toolbox that performs a detailed analysis of particle data, from several space missions. This module of the AIDApp will deal, automatically, with very large particle dataset. Plasma data are inherently 4-dimensional (one time coordinate, and 3 dimensions in the velocity space). These data possibly contains the most unexplored treasure of information, important for the physics of the heliosphere and, in general, for astrophysical processes. In this regard, the AIDApp, because of its versatility, will represent the first publicly available performing software for energetic particles analysis. This algorithm will help to identify possible correlations between fluctuating fields, structures, and acceleration of energetic particles.

Report on the Mathematical analysis of NNs and recommendations for developers

An extension on the benchmarks used for D2.1 is used for the analysis of multiple types of NNs that will be added to the AIDApp package. The benchmarks will be based on the spacecraft data and the exact applications required by the Consortium: time series, event catalogs and images. This deliverable will show a detailed analysis of the internal operations of the NNs. This in-depth analysis allows to select the best NN topology to a particular space data application. Practical recommendations are presented for each of the possible space application in the project, and for future users of the AIDApp package.

Review of the application of Machine Learning techniques to OpenGGCM global magnetospheric simulations

In this deliverable we will review in a paper the results of our Machine Learning activities centred on OpenGGCM magnetospheric simulations OpenGGCM magnetospheric simulations extend for hours in time and reproduce the entire magnetosphere For this reason they can be used to test and validate in a controlled environment ML techniques that will be eventually applied to spacecraft observations within other work packagesAdditionally OpenGGCM simulations are used for scientific investigation The event classification methods we train on the simulations can be used to assist the numerical scientist in the identification of events of interest in large scale simulations making their daytoday work extremely more efficient

Report on optimization and testing of AIDApy on different hardware architectures

This report will summarize the optimization and testing results of the AIDA ML engine in different hardware architectures The simulation and performance results regarding the implementation of AIDApy in commercially available state of the art HW platforms used for deep learning like NVIDIA GPUs Intel Xeon Phi processor will be added In addition testing results for the compatibility of AIDA ML and AIDApy with different hardware platforms carried out in this task will be described

Minutes of the 2nd Advisory Board meeting

Minutes of the 2nd Advisory Board meeting

Final report of WP2: User's manual for the AI software framework

We will produce a summarized report on the selected AI framework based on the performance analysis on multiple types of NN architectures made for D21 and the mathematical analysis of D22 A benchmarking procedure for the evaluation of the selected NN topology or a new one proposed by future users will be set analytically

Report on Hyper-Parameter Optimization and novel AI algorithms

This report presents how HyperParameter Optimization will be used in the AIDApy package It contains the description of the tools selected the method of integration with the AIDApy package and examples on how to use the HPO tools The report will also give an overview of the novel AI algorithms explored in the consortium and the possible extensions to even more algorithms

Software documentation (user guide, ICDs) released

The user guide for AIDApp both Tool 1 and Tool 2 will be completed by M16 and delivered to the AIDA project These tools will include a description of the openaccess archives used by AIDA and of the data formats eg CDF for in situ data FITS for remote imaging data etc A glossary will be produced to harmonize terminology across all AIDA tools

Verification and validation report of AIDApy

This report generated an the end of Task 2 of WP9 will summarize the integration validation and verification procedures carried out including all the related activities for testing AIDApp functionalities All relevant application scenarios of AIDApy functionalities developed within WP2 and WP3 will be carried out on the AIDApy platform set up in Irida Labs premises and all findings will be added in the report

Preliminary optimization report of AIDA ML engine

The report at the end of Task 1 of WP9 will summarize optimization procedures followed for both storage space and computations To this end a list of techniques utilized for reducing the computational complexity and the parameters used by the AIDA ML engine will be presented This report will also cover issues dealing with the design and development of the AIDA ML engine carried out in WP2 and the AIDApp in WP3 and will include design and architecture selection hardware testing evaluation of the ML training procedure evaluation of the downloadupload procedure and support in data selection during training of the ML engine

Report on dimensionality reduction

In this report we we will summarize our efforts of applying techniques of dimensionality reduction to space datasets such as solar wind properties In particular we will test and report on the robustness of a dimensionality reduction approach for different solar activities and geomagnetic conditions The goal of dimensionality reduction is to find nontrivial robust relationship between observables that allow to describe a system through a minimal set of informative variables which can be nonlinear functions of the original set of observables The are two purposes the first is to alleviate the computational cost of training ML techniques the second is to understand the relationship between variables from a physical perspective

Report on modern HPC implementation

The final delivery of this WP will be the High Performance Computing HPC implementation A number of Pythonrelated libraries exist for the programming of solutions either employing multiple CPUs or multicore CPUs in a symmetric multiprocessing or shared memory environment or potentially huge numbers of computers in a cluster or grid environment The module adapted for AIDA will offer an interface similar to the multiprocessing providing a complete abstraction of the startup process and the communication and load balancing layers It will works over MPI with mpi4py or PyMPI supporting D42 D43 as well as other deliverables of AIDApy

Data Management Plan version 1.0

The Data Management Plan (DMP) is a live document. The first version will be reported on month 6 and will continue to evolve through the project. It will include all the decisions taken by the consortium regarding: handling of data during and after the project, type of data collected and processed, standards applied, type of access, and plans for its distribution during and after the project.

Data Management Plan version 2.0

Second version of the DMP for the project AIDA.

Graphic image, dissemination and outreach plan

The first step of the dissemination is the definition of a logo and graphic image of the project with colour palette and font to be used for all the following actions, in order to give to all partners a guideline for graphics and start drawing the layout of the website. For this task is important that all partners will share materials as soon as will be available. All the available material will be organized and shown in the best way will be possible with images, video created ad-hoc, news and articles, flyer/brochure and poster for specific events.

Indicators of penetration in traditional and social media and correcting efforts

The dedicated web site both with the most popular social media like Twitter, Facebook, LinkedIn and Research Gate, will be the online presence to be maintained updated. We will also regularly monitor the online presence of AIDA in social media by means of indicators of penetration that we will step by step choose and upgrade and we will correct the efforts to improve the online presence.

Indicators gathered from short courses, schools and early access

Cineca in collaboration with UNIPI will organize schools to spread AIDA project results by using theory lessons and hands on sessions for the next generation of scientist After having significant projects results Cineca will provide spaces for the schools that will be organized with the collaboration and availability as teachers of all partners

Definitions of the software management standards and testing suit

In this document we will report the decisions taken for the management and automatic testing of the AIDApp and AIDAdb packages. We will perform a selection of the version control system, its physical location, and set up all the necessary access permissions. We will also document the selection of specific small benchmark cases, representative of the functionalities of the AIDApp and AIDAdb products, to perform automatic tests at a given frequency (nightly, weekly), ensuring a continuous follow up of the performances of the products.

Multi-media press kit

All material will be collected during the project and processed in order to make visible to all media selected In particular the progress of the work of the partners will be highlighted by creating specific video dedicated also to specific achievement andor progress of the work Inside the website and the chosen social media the press information will be shown and make available to best disseminate the project

Front-end Python software

A Python package will be created, able to digest data in any format of interest for the users of space missions archives. The package will offer an high-level interface to existing ML libraries and routines, with different levels of verbosity, in order to accommodate the needs of both expert and non-expert users. The open source code will be made publicly available on a git repository since the start of the project so to attract the interest and possibly the inputs of a large community of coders.

Publicly available database of virtual spacecraft measurements of fields and particle VDs

Virtual spacecraft, in single or multi-point configuration, will be launched through the output of previously selected numerical simulations and will provide synthetic measurements of electric and magnetic fields, particle velocity distributions and their moments (density, mean velocity temperature, heat flux ect.), mimicking real measurements of satellites (time series). In order to launch a virtual satellite across the output data of any kind of simulations, where particle distribution functions and electromagnetic fields are known on grid points, we will implement advanced interpolation techniques in space and time to design the trajectory of the satellite a posteriori (i. e. not during the code run phase) and to collect values of the observables in between adjacent grid points. Trajectories of virtual spacecraft flying through the output of global simulations will be implemented in such a way to reproduce the trajectories of real spacecraft flying into space. These synthetic measurements will be collected and organized in the AIDA database (AIDAdb) and made publicly available.

Open source software to perform the information theoretical analysis

We will develop routines that calculates traditional informationtheory based measures such as mutual information transfer entropy and conditional mutual information There are a few standard algorithms to do that and some less standard one namely based on not binning the distribution We will allow the user to choose which algorithm to us clearly spelling out the different computational cost The default option will be automatically determined given the size of the dataset

Open source python softwares for virtual data processing and for estimating the percentage of success of IA algorithms

As a final deliverable of WP5 in collaboration with all AIDApp workpackages in particular with WP3 and WP4 we will implement open source python softwares for processing and analyzing data collected by virtual spacecraft These algorithms implemented to run Artificial Intelligence analyses on virtual time series will allow to estimate the percentage of success of the AIDapp softwares for the identification of regions of scientific interest Significant comparisons with the output of these analyses performed on real data from satellites will be run to complete the testing phase before the release of the final product

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Publications

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

Author(s): Brecht Laperre, Jorge Amaya, Giovanni Lapenta
Published in: Frontiers in Astronomy and Space Sciences, 7, 2020, ISSN 2296-987X
Publisher: Frontiers Media
DOI: 10.3389/fspas.2020.00039

A multi-fluid model of the magnetopause

Author(s): Roberto Manuzzo, Francesco Califano, Gerard Belmont, Laurence Rezeau
Published in: Annales Geophysicae, 38/2, 2020, Page(s) 275-286, ISSN 1432-0576
Publisher: Copernicus Publications
DOI: 10.5194/angeo-38-275-2020

Characterizing current structures in 3D hybrid-kinetic simulations of plasma turbulence

Author(s): 4. M. Sisti, S. Fadanelli, S.S. Cerri, M. Faganello, F. Califano, O. Agullo
Published in: Astronomy & Astrophysics,, 655, A107, 2021, Page(s) 18, ISSN 0004-6361
Publisher: Springer Verlag
DOI: 10.1051/0004-6361/202141902

Identifying Magnetic Reconnection in 2D Hybrid Vlasov Maxwell Simulations with Convolutional Neural Networks

Author(s): A. Hu, M. Sisti, F. Finelli, F. Califano, J. Dargent, M. Faganello, E. Camporeale, J. Teunissen
Published in: The Astrophysical Journal, 900/1, 2020, Page(s) 86, ISSN 1538-4357
Publisher: IOP Publishing
DOI: 10.3847/1538-4357/aba527

Unsupervised classification of simulated magnetospheric regions

Author(s): Maria Elena Innocenti; Jorge Amaya; Joachim Raeder; Romain Dupuis; B. Ferdousi; Giovanni Lapenta
Published in: Annales Geophysicae, Vol 39, Pp 861-881 (2021), 21, 2021, ISSN 1432-0576
Publisher: Copernicus Publications
DOI: 10.5194/angeo-2021-33

Electron-Only Reconnection in Plasma Turbulence

Author(s): Francesco Califano, Silvio Sergio Cerri, Matteo Faganello, Dimitri Laveder, Manuela Sisti, Matthew W. Kunz
Published in: Frontiers in Physics, 8, 2020, Page(s) 12, ISSN 2296-424X
Publisher: Frontiers Media SA
DOI: 10.3389/fphy.2020.00317

Local Regimes of Turbulence in 3D Magnetic Reconnection

Author(s): G. Lapenta, F. Pucci, M. V. Goldman, D. L. Newman
Published in: The Astrophysical Journal, 888/2, 2020, Page(s) 104, ISSN 1538-4357
Publisher: IOP Publishing
DOI: 10.3847/1538-4357/ab5a86

Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques

Author(s): Manuela Sisti, Francesco Finelli, Giorgio Pedrazzi, Matteo Faganello, Francesco Califano, Francesca Delli Ponti
Published in: The Astrophysical Journal, 908/1, 2021, Page(s) 107, ISSN 0004-637X
Publisher: University of Chicago Press
DOI: 10.3847/1538-4357/abd24b

Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

Author(s): Dimitrios Millas; Dimitrios Millas; Maria Elena Innocenti; Brecht Laperre; Joachim Raeder; Stefaan Poedts; Stefaan Poedts; Giovanni Lapenta
Published in: Frontiers in Astronomy and Space Sciences, Vol 7 (2020), 31, 2020, ISSN 2296-987X
Publisher: Frontiers Media
DOI: 10.3389/fspas.2020.571286

Visualizing and Interpreting Unsupervised Solar Wind Classifications

Author(s): Jorge Amaya, Romain Dupuis, Maria Elena Innocenti, Giovanni Lapenta
Published in: Frontiers in Astronomy and Space Sciences, 7, 2020, ISSN 2296-987X
Publisher: Frontiers Media
DOI: 10.3389/fspas.2020.553207

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Author(s): E. Camporeale
Published in: Space Weather, 2019, ISSN 1542-7390
Publisher: American Geophysical Union
DOI: 10.1029/2018sw002061

Energy conversion in turbulent weakly collisional plasmas: Eulerian hybrid Vlasov-Maxwell simulations

Author(s): O. Pezzi, Y. Yang, F. Valentini, S. Servidio, A. Chasapis, W. H. Matthaeus, P. Veltri
Published in: Physics of Plasmas, 26/7, 2019, Page(s) 072301, ISSN 1070-664X
Publisher: American Institute of Physics
DOI: 10.1063/1.5100125

Statistical Analysis of Ions in Two-Dimensional Plasma Turbulence

Author(s): Francesco Pecora, Francesco Pucci, Giovanni Lapenta, David Burgess, Sergio Servidio
Published in: Solar Physics, 294/9, 2019, ISSN 0038-0938
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s11207-019-1507-6

Decomposition of plasma kinetic entropy into position and velocity space and the use of kinetic entropy in particle-in-cell simulations

Author(s): Haoming Liang, Paul A. Cassak, Sergio Servidio, Michael A. Shay, James F. Drake, Marc Swisdak, Matt R. Argall, John C. Dorelli, Earl E. Scime, William H. Matthaeus, Vadim Roytershteyn, Gian Luca Delzanno
Published in: Physics of Plasmas, 26/8, 2019, Page(s) 082903, ISSN 1070-664X
Publisher: American Institute of Physics
DOI: 10.1063/1.5098888

Parametric Instability in Two-dimensional Alfvénic Turbulence

Author(s): Leonardo Primavera, Francesco Malara, Sergio Servidio, Giuseppina Nigro, Pierluigi Veltri
Published in: The Astrophysical Journal, 880/2, 2019, Page(s) 156, ISSN 0004-637X
Publisher: University of Chicago Press
DOI: 10.3847/1538-4357/ab29f5

Current Sheets, Magnetic Islands, and Associated Particle Acceleration in the Solar Wind as Observed by Ulysses near the Ecliptic Plane

Author(s): Olga Malandraki, Olga Khabarova, Roberto Bruno, Gary P. Zank, Gang Li, Bernard Jackson, Mario M. Bisi, Antonella Greco, Oreste Pezzi, William Matthaeus, Alexandros Chasapis Giannakopoulos, Sergio Servidio, Helmi Malova, Roman Kislov, Frederic Effenberger, Jakobus le Roux, Yu Chen, Qiang Hu, N. Eugene Engelbrecht
Published in: The Astrophysical Journal, 881/2, 2019, Page(s) 116, ISSN 0004-637X
Publisher: University of Chicago Press
DOI: 10.3847/1538-4357/ab289a

Single-spacecraft Identification of Flux Tubes and Current Sheets in the Solar Wind

Author(s): Francesco Pecora, Antonella Greco, Qiang Hu, Sergio Servidio, Alexandros G. Chasapis, William H. Matthaeus
Published in: The Astrophysical Journal, 881/1, 2019, Page(s) L11, ISSN 2041-8205
Publisher: Institute of Physics Publishing
DOI: 10.3847/2041-8213/ab32d9

Characterizing Magnetic Reconnection Regions Using Gaussian Mixture Models on Particle Velocity Distributions

Author(s): Romain Dupuis, Martin V. Goldman, David L. Newman, Jorge Amaya, Giovanni Lapenta
Published in: The Astrophysical Journal, 889/1, 2020, Page(s) 22, ISSN 1538-4357
Publisher: IOP Publishing
DOI: 10.3847/1538-4357/ab5524

ViDA: a Vlasov–DArwin solver for plasma physics at electron scales

Author(s): Oreste Pezzi, Giulia Cozzani, Francesco Califano, Francesco Valentini, Massimiliano Guarrasi, Enrico Camporeale, Gianfranco Brunetti, Alessandro Retinò, Pierluigi Veltri
Published in: Journal of Plasma Physics, 85/5, 2019, Page(s) 1/25, ISSN 0022-3778
Publisher: Cambridge University Press
DOI: 10.1017/s0022377819000631

Interplay between Kelvin–Helmholtz and lower-hybrid drift instabilities

Author(s): Jérémy Dargent, Federico Lavorenti, Francesco Califano, Pierre Henri, Francesco Pucci, Silvio S. Cerri
Published in: Journal of Plasma Physics, 85/6, 2019, Page(s) 1/19, ISSN 0022-3778
Publisher: Cambridge University Press
DOI: 10.1017/s0022377819000758

Simulation of Plasmaspheric Plume Impact on Dayside Magnetic Reconnection

Author(s): J. Dargent, N. Aunai, B. Lavraud, S. Toledo‐Redondo, F. Califano
Published in: Geophysical Research Letters, 47/4, 2020, Page(s) 9, ISSN 0094-8276
Publisher: American Geophysical Union
DOI: 10.1029/2019gl086546

Study of PVI-based diagnostics for 1D time series in space plasmas

Author(s): F. Finelli, S. Perri, M. Sisti, F. Califano
Published in: Astronomy & Astrophysics (A&A), 656, A43, 2021, Page(s) 11, ISSN 0004-6361
Publisher: Springer Verlag
DOI: 10.1051/0004-6361/202141700

Bridging hybrid- and full-kinetic models with Landau-fluid electrons

Author(s): F. Finelli, S. S. Cerri, F. Califano, F. Pucci, D. Laveder, G. Lapenta, T. Passot
Published in: Astronomy & Astrophysics, 653, 2021, Page(s) A156, ISSN 0004-6361
Publisher: Springer Verlag
DOI: 10.1051/0004-6361/202140279