Authors: India Jackson (Georgia State University - Dept. of Physics and Astronomy), Berkay Aydin (Georgia State University - Dept. of Computer Science), Petrus Martens (Georgia State University - Dept. of Physics and Astronomy)

In the rapidly evolving field of heliophysics research, the demand for accessible, scalable, and cost-effective resources is paramount. Helio-Lite, a free, open-source framework operating within the Amazon Web Services (AWS) ecosystem, utilizes its infrastructure and services. Derived from HelioCloud, it supports smaller research groups’ needs, offers essential prerequisites for artificial intelligence (AI) and machine learning (ML) tasks, and acts as a specialized tool for data sharing and computation. Utilizing AWS’s potent data storage and computational capabilities, Helio-Lite integrates customized python kernels for heliophysics and AI/ML, facilitating efficient data analysis and enhancing our understanding of solar phenomena. Key functionalities of Helio-Lite include interactive data extraction modules for Atmospheric Imaging Assembly (AIA) images, Helioseismic and Magnetic Imager (HMI) images, and near real-time space weather data directly from the Database of Notifications, Knowledge, Information (DONKI), alongside a comprehensive examples repository. Notably, Helio-Lite addresses challenges posed by vast solar data volumes by parsing directly from Amazon’s Simple Storage Service (S3) buckets, improving accessibility and efficiency in analysis. Moving forward, Helio-Lite is poised to undergo continuous enhancements aimed at improving user experience and system management.