RESEARCH

FDL Heliolab 2025

Heliolab tackles key problems in heliophysics and space exploration. The not-so-secret success formula is pairing machine learning experts with researchers in Earth science, cyber-physical systems, heliophysics, astrobiology, space medicine and planetary science for an intensive eight week research sprint, held in the summer break of the academic year. Although, we know that the journey from idea through to a final research outcome (tech memo and trained algorithm and data products) takes 12 months - which we call the Heliolab cycle.In 2025, Heliolab will include five exciting challenges in two different formats; Research Challenges and Research and Technology (R&T) Challenges.

The three Research Challenges are low-TRL open research problems which will explore new scientific questions.
The two R&T Challenges have a higher TRL outcome and place more emphasis on modularity, reproducibility, benchmarking and continuous learning - a key concept in ensuring reliability and safety.

Active Region Characterization and Analysis of Dynamics and Evolution

Can we capture and codify the dynamic state of active regions on the Sun to inform downstream chained models? 

The changing features of active regions on the Sun potentially offer clues to inform models of internal drivers of solar activity - some of which may have negative downstream consequences for technology and humans on Earth, Moon or Mars. This challenge will leverage data from solar instruments such as SDO/HMI and 4K imagery of the solar disk to examine active regions in detail.  Employing AI-driven feature extraction, physics-informed helioseismology, and continual learning to derive key parameters, like magnetic field strength and configuration, which will serve as essential boundary conditions for subsequent modeling and can be seen as the “first step in the chain” of understanding.

Decoding Solar Wind Structure

Can we identify and understand the precursors and drivers of solar wind structures, with an aim to forecast these in advance? 

This challenge seeks to identify and understand the solar precursors and drivers of different solar wind structures. The challenge itself will consist of two stages. In the first stage, work from FDL 2021 on clustering and classifying solar wind structures such as switchbacks and high speed streams emanating from coronal holes will be extended and include the latest in-situ Parker Solar Probe and Solar Orbiter data. Both unsupervised and interpretable machine learning approaches will be used to explore the complex nature of solar wind structures. The second stage takes this a step further, using the distinct solar wind structures identified, solar disk imagery, and foundation model embeddings from SDO data to link precursors and drivers on the solar disc to target structures. An aim here is to drive physics informed modelling, including the physics-based HUXt model and magnetic field data. A direct application of being able to link dynamics on the Sun with solar wind structure is being able to inform solar wind prediction and answer key scientific questions, such as the extent of magnetic reconnection in the corona driving observed switchbacks. From a solar wind prediction aspect the modelling of coronal holes directly informs the preconditioning of the solar wind and downstream impacts to the near-Earth geospace environment.

Ionosphere-Thermosphere Digital Twin

Can we resolve local ionospheric disturbances at a global scale?

This challenge aims to develop a global model of Total Electron Content (TEC) by leveraging data from dual-band GNSS receivers and expanding to large, low-level mobile GNSS datasets. The model will support ionospheric scintillation studies and integrate with the FDL-developed Karman-CL pipeline, enabling advanced system integration for forecasting.

The primary goal is to establish a framework that unifies individual models to study magnetospheric-ionospheric coupling. Building on prior success in forecasting neutral density, this project extends its application to model ionospheric scintillation—critical for understanding space weather impacts on communication and navigation systems.

The scientific value lies in developing an AI-driven model connecting processes from the Sun to the solar wind, magnetosphere, upper atmosphere, and ground. Key innovations include enabling inter-model communication, where outputs from one AI model inform another, and the sheer scale of the challenge in getting mobile GNSS data integrated. The main risks involve ensuring effective integration and managing the complexity of these interconnected systems.

Multimodal Flare Prediction

How can we synthesize data from multiple NASA missions to develop a 4π virtual flare monitor?

This challenge aims to develop a virtual 4π GOES soft X-ray (SXR) flare monitor using front- and far-side EUV images to enable continuous solar flare prediction across the entire solar disc, enhancing space weather forecasting. By merging this virtual instrument output with ensemble forecasting and integrating observational data, the system will continuously update the probability distribution of imminent solar eruptions across the Sun.

The scientific value lies in overcoming current limitations in flare monitoring. For over 30 years, GOES SXR data has been essential for classifying flare strength and informing forecasts. However, existing methods focus only on front-side observations, leaving significant limb and far-side data underutilized.

Key innovations include using EUV data to assess whether an event is eruptive, improving forecasting capabilities. A potential risk is inadequate uncertainty quantification of the EUV flare index for reliable research and operational use but overcoming this, the result would be transformative for human space exploration.  Once developed, this virtual instrument will be able to integrate and be driven by foundation models, such as HelioFM.

Orchestrator

Can we enable confidence in model predictions by including context from multiple models?

Enabling a decision intelligence system: this challenge aims to establish a framework that enables seamless integration, interaction, and fine-tuning of models developed across multiple Heliolab research challenges. This framework will allow AI agents to interact with diverse models—adapting inputs, running complex workflows, and sourcing outputs—thereby enhancing flexibility, interoperability, and scientific discovery. A key innovation is enabling the use of embeddings from foundation models, such as HelioFM, to enrich model interactions and support advanced reasoning capabilities.

This challenge is critical for realizing the full potential of RADIANT, serving as a departure point for future model integration across the community. Providing a common platform and specified framework will enable RADIANT to link models. The goal is to create an adaptable environment where models can be fine-tuned for specific objectives, and AI agents can autonomously manage complex workflows as a stepping stone to a full sensing-reasoning system for the solar-terrestrial environment.