Mapping the Sun’s Breath: AI Connects Solar Imagery to Space Weather

Author: Denis Avetisyan

Researchers are harnessing the power of advanced artificial intelligence to bridge the gap between observations of the Sun and the resulting conditions in space around Earth.

The distribution of solar wind classifications varies significantly across datasets, indicating a dependency on observational context and highlighting the need for robust categorization methods to ensure consistent analysis of heliospheric phenomena.

This study presents a novel framework utilizing pretrained foundation models and positional encoding to classify solar wind phenomena by linking solar imagery with in-situ plasma measurements.

Predicting space weather events—critical for safeguarding Earth-orbiting assets and ground infrastructure—remains challenging due to the complex and variable nature of solar wind phenomena. The study ‘CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena’ introduces a novel framework that bridges remote sensing observations with in-situ measurements by adapting pretrained foundation models and employing neural fields with positional encoding. This approach generates embeddings capable of classifying solar wind structures, demonstrating the feasibility of linking solar imagery to plasma properties measured by spacecraft like Parker Solar Probe. While current classification performance is limited by labeling constraints, does this work represent a crucial step towards more accurate and reliable space weather forecasting capabilities?

The Sun’s Emanations: A Foundation for Understanding

The Sun isn’t a static beacon; it constantly releases a stream of charged particles known as the solar wind, a phenomenon with far-reaching consequences. This outflow, composed primarily of electrons and protons, isn’t uniform; it varies in speed, density, and magnetic field strength, creating a complex interplanetary environment. When directed towards Earth, the solar wind interacts with the planet’s magnetic field, often causing geomagnetic storms. These storms can disrupt satellite communications, damage power grids, and even produce stunning auroral displays. Understanding the solar wind is therefore crucial not only for comprehending the Sun’s outer atmosphere, but also for protecting critical technological infrastructure and ensuring the continued functionality of space-based assets. Its influence extends beyond Earth, impacting the atmospheres and surfaces of other planets throughout the solar system, highlighting its pervasive role in shaping the interplanetary landscape.

Coronal holes, vast regions of cooler, less dense plasma in the Sun’s corona, are recognized as the primary sources of the fast solar wind. Unlike the slower, more variable wind originating from the Sun’s equatorial regions, the fast solar wind streams continuously from these open magnetic field structures, essentially ‘holes’ where plasma isn’t contained by strong magnetic forces. These areas, appearing dark in extreme ultraviolet images, exhibit lower temperatures – around $10^6$ Kelvin compared to the corona’s typical millions of degrees – and reduced density, allowing particles to escape the Sun’s gravity more easily. The magnetic field lines within coronal holes are largely open, extending far into interplanetary space, effectively channeling the outflowing plasma and accelerating it to speeds exceeding 700 kilometers per second. Consequently, understanding the formation, evolution, and location of these features is crucial for predicting space weather events and their potential impact on Earth’s technological infrastructure.

The solar wind’s behavior – its speed, density, and magnetic field strength – is fundamentally dictated by the conditions within the coronal holes from which it originates. These expansive regions of open magnetic field lines act as the source for fast solar wind streams, and variations in their size, shape, and magnetic field configuration directly translate into fluctuations in the wind’s characteristics. A larger coronal hole, for instance, typically results in a more intense and sustained outflow. Furthermore, the magnetic field strength and direction at the base of the hole imprint themselves onto the solar wind, influencing its interaction with planetary magnetospheres – including Earth’s – and driving phenomena like geomagnetic storms and auroral displays. Understanding these origins is therefore crucial not just for characterizing the solar wind itself, but for predicting its impact on space weather and the broader heliosphere.

The model accurately predicts magnetic footpoints from the Parker Solar Probe (indicated by crosses) for diverse solar wind sources, including coronal holes, sector reversals, and streamer belts as observed by SDO/AIA.

Positional Encoding: A Necessary Expansion of Dimensionality

Conventional techniques in 3D modeling and representation often exhibit limitations when capturing high-frequency positional details. These methods, such as mesh-based representations or volumetric grids, typically require substantial computational resources to represent fine geometric features, and often resort to approximations that sacrifice accuracy. Specifically, the inability to efficiently model rapid changes in position or surface normals leads to blurring of details and reduced fidelity in reconstructions. This is particularly problematic when dealing with complex scenes or objects containing intricate structures, as the discretization inherent in these traditional approaches introduces artifacts and limits the overall representational capacity of the model. Consequently, capturing and representing high-frequency positional data remains a significant challenge in numerous applications, including computer graphics, robotics, and medical imaging.

Neural fields address limitations in representing positional data by mapping input coordinates to a high-dimensional space before processing. This transformation, achieved through the use of functions like multi-layer perceptrons (MLPs), effectively increases the complexity of the representable functions without altering the network architecture. By encoding position in this higher-dimensional space, the neural field can more accurately capture high-frequency details and intricate geometric features. This approach contrasts with traditional methods that rely on explicitly discretizing space or using lower-dimensional representations, which often struggle to maintain detail and can lead to aliasing artifacts. The increased representational power enables the reconstruction of complex scenes and shapes with greater fidelity and allows for continuous, differentiable representations of geometry.

Fourier Features, when integrated into neural fields, enhance positional encoding by mapping input coordinates to a higher-dimensional space using a series of sine and cosine functions with varying frequencies. This transformation, defined as $ \gamma(x) = [cos(2^0 \pi x), sin(2^0 \pi x), cos(2^1 \pi x), sin(2^1 \pi x), …]$, allows the network to more easily learn high-frequency details in the input data. The use of multiple frequencies ensures that the representation captures positional information at varying scales, improving the accuracy and efficiency of the neural field in modeling complex scenes or functions. This approach avoids the limitations of traditional positional encodings which often struggle with high-frequency details due to their limited bandwidth.

The proposed model classifies solar wind by integrating features extracted from pre-trained solar images with positional information from the Parker Solar Probe, utilizing a classification head and focal loss for training.

Skip Connections: Preserving Detail Through Direct Pathways

The skip-connection head architecture is implemented to preserve fine-grained positional information as data progresses through the network. This design incorporates direct connections that bypass certain layers, allowing the original input data, representing detailed positional cues, to be directly added to the output of subsequent layers. This mitigates the loss of detail that can occur in deep networks due to repeated transformations and non-linearities, effectively creating a “shortcut” for positional information to flow unimpeded throughout the processing pipeline. The resulting architecture enhances the network’s ability to accurately represent and utilize precise positional data during rendering or other downstream tasks.

The skip-connection head utilizes the ResNet (Residual Network) architecture, a deep learning model distinguished by its capacity to effectively train very deep networks. ResNet achieves this through the implementation of residual connections, or “skip connections,” which allow gradients to flow more easily through the network during training, mitigating the vanishing gradient problem. This enables the network to learn more complex features and representations, increasing its overall representational capacity. Specifically, ResNet’s ability to learn identity mappings through these skip connections allows it to preserve information from earlier layers, contributing to improved performance in tasks requiring high fidelity detail.

Skip connections, implemented within the network architecture, are directly informed by the principles of Neural Radiance Fields (NeRF). In NeRF, detailed positional information is crucial for high-fidelity rendering; similarly, these skip connections provide a direct pathway for fine-grained details to propagate through the layers of the network without being subjected to repeated transformations. This unimpeded flow of information mitigates the loss of detail that can occur in deep networks, preserving critical positional data and ultimately improving the overall accuracy of the model’s output by ensuring features from earlier layers are readily available to later stages of processing.

Training loss curves reveal that fine-tuning a pretrained backbone alongside a linear head consistently outperforms training only the head or initializing both randomly.

Optimization and Convergence: A Robust Training Regimen

The model’s training process leverages the Adam optimizer, a sophisticated algorithm designed to efficiently navigate the complex landscape of error minimization. Unlike traditional gradient descent methods, Adam adaptively adjusts the learning rate for each parameter, effectively accelerating convergence and preventing oscillations. This is achieved by maintaining both a first and second moment estimate of the gradients, allowing for both direction and magnitude to be refined with each iteration. Consequently, the model quickly identifies optimal weights, leading to enhanced performance and stability as it learns from the training data. The algorithm’s inherent efficiency is particularly crucial when working with large datasets, such as the nearly one million samples utilized in this study, enabling robust and timely model development.

Recognizing that datasets often exhibit uneven class representation – where certain categories significantly outnumber others – the study incorporated the Focal Loss function to refine model training. This loss function strategically down-weights the contribution of easily classified examples, allowing the model to concentrate on the more challenging, minority classes. By focusing learning on these underrepresented instances, Focal Loss mitigates the risk of the model becoming biased towards the dominant classes and ensures a more robust and generalized performance across the entire dataset. The result is a model less prone to errors on infrequent but potentially critical categories, enhancing its overall reliability and practical utility.

The training regimen, leveraging the Adam optimizer and Focal Loss, demonstrably enhanced the performance of both the skip-connection and linear head architectures. Through 50 training epochs, the model consistently refined its predictive capabilities; however, to prevent overfitting and maximize generalization, an early stopping mechanism was implemented. This strategy monitored validation loss, halting the training process when improvements plateaued, thus ensuring both improved accuracy and stability in the final model. The combined effect of this optimization and the robust dataset—comprising nearly one million samples and over 13,000 test instances—resulted in a highly effective and reliable system.

The model’s performance is underpinned by a substantial dataset, comprising nearly one million individual samples used for both training and validation. This large volume of data allows the network to learn complex patterns and generalize effectively to unseen examples. Crucially, the dataset’s robustness is further assured through a dedicated test set of over 13,000 instances, enabling a rigorous and statistically significant evaluation of the model’s capabilities. Such a sizable evaluation set minimizes the risk of overfitting and provides a reliable measure of the model’s true performance on real-world data, ensuring a high degree of confidence in its predictive accuracy.

Fine-tuning a pretrained Masked Autoencoder (MAE) decoder significantly improves image reconstruction quality, as demonstrated by its ability to accurately recreate a Solar Dynamics Observatory/Atmospheric Imaging Assembly 193 Å image (shown on the left) compared to the initial reconstruction (middle) and the fine-tuned result (right).

Towards Foundation Models: A Future of Predictive Space Weather

The pursuit of generalized intelligence is gaining momentum through the development of foundation models – artificial intelligence systems pretrained on massive datasets using self-supervised learning techniques. Unlike traditional models designed for specific tasks, these systems learn underlying patterns and representations directly from unlabeled data, enabling them to adapt to a wide range of downstream applications with minimal fine-tuning. This approach mirrors human learning, where individuals acquire broad knowledge before specializing in particular areas. By exposing the model to vast amounts of data, it develops a robust understanding of complex relationships, allowing it to generalize beyond the specific examples it was trained on. The potential extends beyond conventional AI, offering a pathway to systems capable of reasoning, problem-solving, and even creative endeavors with a level of flexibility previously unattainable.

The application of foundation models to space weather represents a shift towards predictive capabilities for complex solar phenomena. These models, trained on extensive datasets of solar imagery and magnetic field measurements, learn to recognize patterns indicative of coronal hole development and the subsequent propagation of solar wind. By identifying these features, the models can forecast the evolution of coronal holes – regions of open magnetic field that are sources of high-speed solar wind – and anticipate the arrival of resulting geomagnetic disturbances at Earth. This predictive ability extends to estimating the intensity and direction of the solar wind, crucial parameters for understanding and mitigating the impact of space weather on technological systems, including power grids and satellite operations. The models do not simply extrapolate past behavior, but rather learn an underlying representation of solar dynamics, potentially enabling predictions beyond the limitations of traditional physics-based simulations.

Advancements in space weather forecasting promise a future with enhanced protection for vital technological assets. More accurate predictions of geomagnetic storms – disturbances in Earth’s magnetosphere – are now within reach, safeguarding critical infrastructure like power grids and communications networks, as well as the numerous satellites essential for modern life. Initial studies utilizing foundation models demonstrate an accuracy of approximately 30% in forecasting these events, representing a significant, though preliminary, step forward. Researchers acknowledge current limitations stem from the challenges of labeling complex space weather phenomena and inherent constraints within the model’s architecture, areas targeted for continued refinement to improve predictive capabilities and overall reliability.

The pursuit of accurate solar wind classification, as detailed in the presented work, demands a rigorous foundation—a principle echoed by Andrew Ng when he stated, “AI is not about replacing humans; it’s about augmenting human capabilities.” This framework, utilizing pretrained foundation models and a novel approach to positional encoding, isn’t simply seeking to automate the process; it aims to enhance the predictive power of space weather forecasting by bridging the gap between remote sensing imagery and in-situ measurements. The elegance lies in the mathematical transformation of data, allowing for a more provable and robust classification system, aligning with the core philosophy that a solution’s validity isn’t determined by empirical success alone, but by its inherent logical structure.

Beyond the Corona: Charting Future Courses

The demonstrated coupling of remotely sensed coronal imagery with in-situ plasma measurements represents a logical, if belated, step. The current framework, while promising, remains fundamentally a proof-of-concept. The true test lies not merely in classification accuracy – any sufficiently complex function approximator can achieve that – but in demonstrable improvements to space weather forecasting skill. If predictive power doesn’t materialize beyond statistical baseline, the elegance of the architecture becomes largely academic. One suspects the devil, as always, resides in the invariants – or lack thereof – governing the solar wind itself.

Future work must address the inherent limitations of relying solely on the visual cortex of a neural network to interpret complex plasma dynamics. Integrating physics-informed constraints – beyond simple positional encoding – could prove critical. Furthermore, the reliance on pre-trained foundation models introduces a potential fragility. If these models were to exhibit unexpected biases or fail to generalize to novel solar conditions, the entire edifice could crumble. A truly robust system demands a more fundamental, theoretically grounded approach.

If the observed performance feels somewhat magical, it is likely because the underlying principles governing the connection between coronal structure and in-situ measurements remain poorly understood. The current work illuminates a path, but the ultimate destination – a predictive theory of space weather – remains distant. The challenge, as always, is to move beyond correlation and towards genuine understanding.

Original article: https://arxiv.org/pdf/2511.09843.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/