What AIDA is about

AIDA brought a transformational innovation to the analysis of heliophysics data in four steps.
First, AIDA developed a new open source software called AIDApy written in Python (a free language) and capable of collecting, combining and correlating data from different space missions. AIDApy wanted to replace mission-specific tools written for costly languages (such as IDL) that excludes many scientists, students and amateur space enthusiasts from exploring the data, with a much-needed single platform where methods are shared and continuously improved by the whole community.
Second, AIDA introduced modern data assimilation, statistical methods and machine learning (ML) to heliophysics data processing. Unlike traditional methods based on human expertise, these methods relies on statistics and information theory to extract features that are hidden in the data.
Third, AIDA combined real data from space missions with synthetic data from simulations developing a virtual satellite component for AIDApy. This feature was demonstrated in the comparison with existing mission data and in the planning of new missions (e.g. ESA’s THOR).
Fourth, AIDA deployed in AIDApy methods of Artificial Intelligence (AI) to analyse data flows from heliophysics missions.
This task required bridging together competences in computer science and in heliophysics and pushed well beyond the current state of the art in space data analysis, connecting space researchers with AI, one of the fastest growing trends in modern science and industrial development.
AIDA used the new AIDApy in selecting key heliophysics problems to produce a database (AIDAdb) of new high-level data products that include catalogs of features and events detected by ML and AI algorithms. Moreover, many of the AI methods developed in AIDA themselves represent higher-level data products, for instance in the form of trained neural networks that can be stored and reused as a database of coefficients.