The Use of AI in Operational Space Weather Missions

Artificial intelligence (AI) has increasingly found its way into various space applications, with edge AI proving particularly useful in certain scenarios. Edge AI refers to AI that is implemented at the edge of a network, meaning it can operate locally without the need for a constant internet connection. This is particularly useful in space applications, where connectivity can be very limited or even non-existent during long periods of time.

One application of edge AI in space is solar observation satellites. These satellites are typically equipped with a variety of sensors that collect data about the Sun, such as images of solar flares and coronal mass ejections (CMEs). However, the limited communication capabilities of these satellites can be a hindrance to their performance and commercial applications. Edge AI can help to overcome this limitation by allowing the satellite to process and analyze data locally, rather than constantly transmitting it back to Earth for processing.

The ESA VIGIL Mission is a space weather mission that will monitor the Sun from the L5 Lagrange point, and the first of its kind to do it operationally from that position. It will provide early warnings of solar storms and help to protect critical infrastructure on Earth.. This will allow the mission to provide uninterrupted observations of the Sun from a perspective ahead of the Earth in its translation journey around the Sun. The VIGIL mission will carry a suite of remote sensing instruments to study the Sun, including a coronagraph, a magnetograph and a heliospheric imager; it also includes in situ instruments, a magnetometer, and a plasma analyzer, in order to aid the forecasting capabilities

The VIGIL mission is tasked to provide early warnings of solar storms and ejections, which can disrupt power grids, communications, and navigation systems on Earth.

For the VIGIL mission, ESA has developed a dedicated “computational memory” space grade hardware and an Artificial Intelligence ensemble system tool to automatically classify solar flares. The computational memory will store the data collected by the VIGIL mission's instruments. The tool to automatically classify solar flares will use this data to identify and classify solar flares. The tool uses the following information to classify solar flares:

• Active region (AR): An AR is a grouping of sunspots on the Sun's surface.
• Sunspot images: Sunspot images are used to determine the number and size of sunspots in an AR.
• Magnitude level: The magnitude level of a flare is a measure of its intensity.
• Number of spots: The number of spots in an AR is a good indicator of the flare's potential intensity.
• Spots class: The spots class is a measure of the magnetic complexity of the sunspots in an AR.
• Observed time: The observed time is the time at which the flare was observed.

The tool first determines, from a single point of view, if an AR is present. If it is, then the tool proceeds to determine the magnitude level of the potential flare. The magnitude level is then used to predict the flare class.

The tool also determines if a running solar flare is associated with a coronal mass ejection (CME). A CME is a large expulsion of plasma and magnetic field from the Sun. Data from several points of view in the Solar System would increase the accuracy of the CME propagation model.

Data ensemble techniques proposed in this work can be generalized to multiple instrument ensemble, and even multi-point observation ensemble within the Solar System, with the purpose of increasing not only accuracy (as usually focused) but also increased robustness of a system-of-systems, in this case, the multi-satellite Space Weather Network Alert System.

Hardware needed to run the applications is readily available today for both on-ground and on the edge. The different hardware options range from high-TRL Radiation-Hard-by-Design classical processors with extensive flight heritage to up-screened commercial grade devices. Performance measurements are available for both ends of the spectrum and the devices can therefore be chosen for the desired reliability level. An important aspect of integrating AI into systems requiring high reliability is the ability to qualify both the hardware and the software stacks, including the operating systems. Both fully space qualified and fully commercial operating systems have been used for characterizing the performance of the applications to ensure that the appropriate reliability can be achieved.

This hardware has a very small SWaP impact for edge applications and allows for in flight seamless update of inference code, adapting to unforeseen changing mission conditions and to opportunistic science or operational goals.

The developed hardware can perform inferences very quickly, which could allow for oversampling on board the instruments. It can also be used on ground data centers to provide 24/7 observations. The system has demonstrated promising accuracy and performance in its first results, and it has shown the ability to detect CMEs and predict the possibility of CME occurrence from active areas on the Sun.