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Vlasov methods in space physics and astrophysics.

ABSTRACT: This paper reviews Vlasov-based numerical methods used to model plasma in space physics and astrophysics. Plasma consists of collectively behaving charged particles that form the major part of baryonic matter in the Universe. Many concepts ranging from our own planetary environment to the Solar system and beyond can be understood in terms of kinetic plasma physics, represented by the Vlasov equation. We introduce the physical basis for the Vlasov system, and then outline the associated numerical methods that are typically used. A particular application of the Vlasov system is Vlasiator, the world's first global hybrid-Vlasov simulation for the Earth's magnetic domain, the magnetosphere. We introduce the design strategies for Vlasiator and outline its numerical concepts ranging from solvers to coupling schemes. We review Vlasiator's parallelisation methods and introduce the used high-performance computing (HPC) techniques. A short review of verification, validation and physical results is included. The purpose of the paper is to present the Vlasov system and introduce an example implementation, and to illustrate that even with massive computational challenges, an accurate description of physics can be rewarding in itself and significantly advance our understanding. Upcoming supercomputing resources are making similar efforts feasible in other fields as well, making our design options relevant for others facing similar challenges.

SUBMITTER: Palmroth M

PROVIDER: S-EPMC6319499 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Vlasov methods in space physics and astrophysics.

Palmroth Minna M Ganse Urs U Pfau-Kempf Yann Y Battarbee Markus M Turc Lucile L Brito Thiago T Grandin Maxime M Hoilijoki Sanni S Sandroos Arto A von Alfthan Sebastian S

Living reviews in computational astrophysics 20180816 1

This paper reviews Vlasov-based numerical methods used to model plasma in space physics and astrophysics. Plasma consists of collectively behaving charged particles that form the major part of baryonic matter in the Universe. Many concepts ranging from our own planetary environment to the Solar system and beyond can be understood in terms of kinetic plasma physics, represented by the Vlasov equation. We introduce the physical basis for the Vlasov system, and then outline the associated numerical ...[more]

PMID: 30680308

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Project description:With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform (www.spedas.org), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have "crib-sheets," user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer's Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its "modes of use" with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans. Electronic Supplementary Material:The online version of this article (10.1007/s11214-018-0576-4) contains supplementary material, which is available to authorized users.

Project description:Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of "black box" or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.

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Vlasov methods in space physics and astrophysics.

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Vlasov methods in space physics and astrophysics.

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