The Solar Information Processing workshop series aims at improving the science return of solar and heliospheric missions by bringing together different communities (solar and space scientists, statisticians, and data processing experts) and addressing the data analysis issues of these missions.
The meeting will last for four full days (Monday 18/08- Thursday 21/08). The first day will focus on statistical methods and their application in space science: Bayesian statistics, multivariate analysis, and an introduction to most recent machine learning algorithms.
The next three days will be dedicated to plenary talks in the morning and to splinter and poster sessions in the afternoon. One afternoon will be devoted to an informal problem solving session, with the aim to actively promote the interactions between solar and space scientists, statisticians, and signal processing experts.
Within the splinter groups researchers are given the opportunity to describe in details their efforts and gather feedback. Leaning on previous accomplishments done by the SIP community in the past decade the splinter sessions in SIP7 will address four scientific issues related to the solar variability and its impact on the Sun-Earth system:
- Optimal combination of in-situ and imaging data, as required for data exploitation of the upcoming Solar Orbiter mission.
Solar Orbiter will unite a comprehensive suite of remote sensing and in situ instruments on the same platform. But how to jointly analyze the heterogeneous data sets? This splinter will discuss how to best combine in-situ and image data, in view of addressing question such as: the origin, evolution, and propagation of Coronal Mass Ejection (CME) through the heliosphere and the link between CME and Interplanetary Coronal Mass Ejection. 3D reconstruction of CME will also be discussed. Finally, the optimal exploitation Solar Orbiter data must be supported by either common data analysis tools or by making existing tools work together. This splinter will address the issue of a common data analysis environment.
- Prediction of solar eruption.
Three important challenges for space weather application are the prediction of solar flares, the prediction of CME arrival and geoeffectiveness, and the prediction of arrival of solar energetic particles. Combination of proper observations (e.g. vector magnetogram vs line of sight magnetogram) and appropriate processing methods will be discussed. For example, the flare prediction using magnetogram information can be cast as a learning task. Modifying off-the-shelf algorithms to take into account the peculiarities of flare prediction is necessary to maximize the prediction performance.
- Tracking of small-scale magnetic features.
Magnetic feature tracking is a fundamental component of many types of data analysis. It allows for the characterization of the statistical parameters of the magnetic field, the derivation of detailed information about the solar dynamo, the estimation of motion field from a time series of images, and the use of this motion field information in driving boundary condition of semi-empirical MHD models. This splinter session will discuss the latest advances on small scale feature tracking, focusing mainly on magnetic tracking, but also including studies on tracking of small elements in the solar corona.
- Origin of variability and prediction of solar wind.
The solar wind is often divided into two elements: the fast wind that emanates from coronal holes, and the slow wind that is more commonly found in the plane of the ecliptic. The fast wind is smooth and even, while the slow solar wind is variable on time scales from minutes to hours. Understanding the difference is central to a cluster of important problems in heliophysics, including: identifying the source location and accelerating mechanism of the wind; determining whether the variability is an imprint of a variable origin or of turbulent processing enroute; and understanding how the wind connects coronal phenomena to Earth and the other planets.