FDL X Helio

FDL-X is a public private partnership with NASA, SETI Institute, Trillium Technologies, Google Cloud, NVIDIA and leaders in commercial AI. This Heliophysics pilot initiative is a derivative of the successful Frontier Development Lab (FDL) and will develop integrated applied AI research and technology for Heliophysics applications.


There will be three FDL-X 2023 teams, each team will be made up of three participants with a mix of AI/ML specialists, heliophysics scientists and software engineers, and a faculty of experts will be assembled around each of the challenges.  The teams will establish close links with scientists, researchers, industry organizations, partners, external experts and stakeholders.

1. Thermospheric Drag and Atmospheric Density Projections

Can we improve our ability to model thermospheric drag (e.g. KARMAN, FDL 2021) by integrating EUV irradiance models? This project will build upon the EUV virtual instrument for observing in this wavelength range from 9 narrowband EUV imaging channels (FDL 2018) and the 4π view of the Sun (using four EUV channels; FDL 2022). As four EUV channels may not contain sufficient information to model the solar spectral irradiance, the team may need to leverage additional FDL outputs, including an image-to-image translation approach (to reconstruct missing EUV channels) and auto-calibration techniques (FDL 2019).

2. Analysis Ready Data of EUV Solar Spectral Irradiance

Can we develop analysis-ready data of EUV Solar Spectral Irradiance for the community? The proposed project would build upon the Solar Dynamic Observatory’s Machine Learning Dataset (SDOML) and FDL 2022FDL 2022 (a 4π view of the Sun across 4 EUV channels) to provide a 4π dataset for EUV solar spectral irradiance among others.

3. Hyperlocal Geoeffectiveness

The project goes after one of the ‘holy grails’ of space weather forecasting; to provide reliable forecasts of space weather effects at a regional level beyond nowcasting. The project builds on FDL’s DAGGER pipeline pursuing a hybrid approach of combining ML/AI and ‘human-in-the-loop’ methodologies to track the geoeffective potential of solar transients from the moment they erupt, through their journey to and impact on earth, and the flow of that energy down to the ground. The end product will provide continuously updated estimates of GIC proxies (DB/dt) around the globe, from the moment the properties of Earth-directed transients have been measured to their impact at Earth 3-4 days later.