Italian Solar Orbiter-SWA Working Group on Machine Learning and Artificial Intelligence
| العنوان: | Italian Solar Orbiter-SWA Working Group on Machine Learning and Artificial Intelligence |
|---|---|
| المؤلفون: | de Marco, R., Alberti, T., Amaya, J., Bruno, R., Califano, F., Camporeale, E., Consolini, G., Foldes, R., d'Amicis, R., Dupuis, R., Franci, L., Guedes dos Santos, L. F., Innocenti, M. E., Jagarlamudi, V. K., Lapenta, G., Laurenza, M., Marcucci, M. F., Narock, A., Papini, E., Perri, S., Perrone, D., Retino, A., Servidio, S., Sisti, M., Sorriso-Valvo, L., Valentini, F. |
| المساهمون: | POTHIER, Nathalie, Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES), Laboratoire de Physique des Plasmas (LPP), Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU) |
| المصدر: | American Geophysical Union American Geophysical Union, 2020, Online, France |
| بيانات النشر: | HAL CCSD, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | ASTROPHYSICS, [SDU] Sciences of the Universe [physics], [SDU]Sciences of the Universe [physics], General or miscellaneous, Instruments and techniques, ASTRONOMY, SOLAR PHYSICS |
| الوصف: | 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/ |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3bd4109ccc0d167b100872a0459ca4c7 https://hal-insu.archives-ouvertes.fr/insu-03560367 |
| رقم الأكسشن: | edsair.dedup.wf.001..3bd4109ccc0d167b100872a0459ca4c7 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |