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In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task. ispartof: Frontiers in Astronomy and Space Sciences vol:10

Despite more than a half-century of study, the basic physical processes responsible for heating and accelerating the solar wind are still not fully understood. These phenomena are at the center of a hot debate that is of great interest for the Solar Orbiter mission (as discussed in details in the Science Activity Plan, SAP) and are strictly linked to the turbulent nature of solar wind fluctuations which cover an extended range of spatial and temporal scales. So the identification of these physical processes is of primary importance for understanding the origins and evolution of the solar wind and its impact on the different bodies of the solar system. Moreover, in a broader context, it would allow also to achieve significant progress in our understanding of stellar astrophysics. Within this context, the Italian Solar Orbiter-SWA Working Group (WGs) on `Multiscale Physics' was created in response to the interest manifested by scientists from several Italian and international institutions on some important topics such as radial evolution of turbulence and Alfvénicity and link between fluid and kinetic scales; solar wind origin; reconnection, intermittency and particle acceleration in the turbulent solar wind (just to cite some of them), with a particular attention to the synergies with other in-situ and remote sensing instruments on board Solar Orbiter and also with other ESA and NASA missions (e.g. L1 observatories and Parker Solar Probe). The `Multiscale Physics' WG involves scientists with an extensive experience in solar wind turbulence and reconnection processes including expertise in data analysis, simulations and modeling. In this contribution, we present the activity developed so far with a particular focus on the scientific cases identified.

International audience; The exponential growth of data volume experienced by astronomy and astrophysics causes new disciplines like machine learning (ML) and data mining (DM) to gain more and more ground in these fields. Applications like clustering, feature selection, automatic classification of events are proving to be a valuable aid in exploiting space data in the era of the synergy between "pure" science and "data-driven" science. The Italian Solar Orbiter-SWA Working Group on Machine Learning and Artificial Intelligence1 together with the European Commission Horizon 2020 project AIDA2, has the scope of applying ML and DM analysis techniques to the Solar Orbiter data. The implementations are numerous. First of all these new techniques can be used to discover unexpected relations between data, can automate tasks so that they can be carried out without human intervention, and can help to forecast physical properties and events. A non-exhaustive list of these activities includes automatic detection of coronal holes in images, automatic recognition of plasma regions, prediction of solar wind properties at 1 AU, classification of solar wind type based on new indicators, analysis of particle velocity distribution functions. In addition, this Working Group will integrate the existing software developed in the context of the various heliospheric missions with the parts regarding Solar Orbiter. These packages are able to handle complex data set with ease and provide statistical analysis and visualization tools. Catalogs of scientific data are also produced, which report, among others, magnetic reconnection and particle acceleration events, detected by routines trained to browse data and select physical processes and features of interest. Here we present the project overview along with the ML and DM tools which will be used to handle and analyse Solar Orbiter data. 1.https://sites.google.com/view/italian-solar-orbiter-swa/ 2.http://www.aida-space.eu/

International audience; We investigated the particle radiation environment at the Lagrangian point L1 and in the near-Earth space by performing a systematic analysis of the proton flux data recorded by the EPAM and CPME particle instruments, aboard ACE and IMP-8 in the energy range ∼ 0.05-5 MeV and 0.29-440 MeV, respectively. We computed the cumulative distribution functions for all the energy channels of each instrument and studied the radiation variation with respect to solar activity. We obtained energetic proton spectra at 90% cumulative probability for different time periods and determined the worst case scenario, which can be used for operational purposes of the ATHENA mission and for Space Weather related hazards.

Contains fulltext : 207560.pdf (Publisher’s version ) (Open Access) Sunspot number reconstructions (SNRs) based on dendrochronologically dated radiocarbon concentrations are analyzed through the Empirical Mode Decomposition (EMD) to provide a deeper characterization of the solar activity long-term periodicities and to investigate the role of the Gleissberg and Suess cycles in the grand minima occurrence. The Gleissberg and Suess cycles, with timescales of 60-120 yr and 200-300 yr respectively, represent the most energetic contribution in SNR records. The EMD approach reveals that these cycles are characterized by multiple scales of variation and that the grand minima sequence is produced by the coupling between Gleissberg and Suess cycles, the latter being responsible for the most intense and longest Spörer-like minima, with typical duration longer than 80 yr.