Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks (2301.06732v7)

Abstract: In the era of space exploration, the implications of space weather have become increasingly evident. Central to this is the phenomenon of coronal holes, which can significantly influence the functioning of satellites and aircraft. These coronal holes, present on the sun, are distinguished by their open magnetic field lines and comparatively cooler temperatures, leading to the emission of solar winds at heightened rates. To anticipate the effects of these coronal holes on Earth, our study harnesses computer vision to pinpoint the coronal hole regions and estimate their dimensions using imagery from the Solar Dynamics Observatory (SDO). Further, we deploy deep learning methodologies, specifically the Long Short-Term Memory (LSTM) approach, to analyze the trends in the data related to the area of the coronal holes and predict their dimensions across various solar regions over a span of seven days. By evaluating the time series data concerning the area of the coronal holes, our research seeks to uncover patterns in the behavior of coronal holes and comprehend their potential influence on space weather occurrences. This investigation marks a pivotal stride towards bolstering our capacity to anticipate and brace for space weather events that could have ramifications for Earth and its technological apparatuses.

• The paper presents a novel deep learning approach using LSTM networks to predict coronal hole areas from solar observation data.
• It incorporates a binary-based Coronal Hole Detection model to process EUV images, thereby improving the accuracy of solar wind forecasting.
• The study reveals significant correlations between expanding coronal hole areas and increased geomagnetic activity, aiding space weather preparedness.

Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks

The paper "Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks" employs a combination of computer vision techniques and deep learning to predict the area of coronal holes, which are pivotal in understanding space weather phenomena. By integrating data-driven methodologies with solar observations, this research offers a novel approach to forecasting coronal hole dimensions, which are key indicators of solar wind intensity and geomagnetic activity.

Coronal Hole Examination and Detection

Coronal holes are regions on the sun's surface characterized by lower temperatures and densities, with open magnetic field lines allowing for the outflow of high-speed solar winds. These areas are visible in extreme ultraviolet (EUV) wavelengths, primarily captured by the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). The binary-based Coronal Hole Detection model (BCH) is a pivotal tool used in the paper to convert these AIA images into data suitable for analysis. By employing multi-filters and converting the resulting images into binary form, the BCH model ensures precise identification of coronal holes for further paper.

Figure 1: The regions used to determine the coronal hole area in the sun, as defined by the binary-based Coronal Hole Detection model (BCH).

Modeling Techniques and Methodology

The paper employs Long Short-Term Memory (LSTM) networks for time-series predictions of coronal hole areas. LSTMs are well-suited for temporal data as they can retain and exploit information from past observations, offering enhanced predictive capabilities over extended sequences. The model leverages data spanning over a decade, capturing full solar cycles, which allows it to recognize long-term patterns in solar activity.

The choice of a deep learning approach, specifically LSTM, underscores the capability of these networks to manage complexities associated with learning from sequential data, making them the ideal candidates for predicting changes in coronal holes over time.

Figure 2: The detection process using multi-filters in coronal hole identification.

Data Analysis and Solar Impact

The long-term data analysis reveals notable trends in coronal hole areas over the observed period. It suggests a gradual expansion in the middle regions, potentially linked to cyclical solar activities. The insights provided by the BCH model enhance understanding of temporal variations in solar activity, correlating these metrics with geomagnetic indices like the AP index.

Figure 3: Variation of coronal hole area for each region over 3857 days.

Predictive Performance

Using the BCH model and the LSTM framework, the paper predicts the dimensions of coronal holes with considerable accuracy. This predictive capability is essential for anticipating the effects of space weather events on Earth's magnetosphere and associated technological systems. A direct correlation between increasing coronal hole areas and subsequent rises in geomagnetic activity has been established, highlighting the practical implications for space weather preparedness.

Figure 4: Accuracy of the LSTM model in predicting coronal hole area over a seven-day period.

Conclusion

The integration of deep learning and computer vision in this paper provides a robust framework for space weather forecasting. By developing a reliable model for predicting coronal hole areas, the research significantly advances the ability to anticipate space weather events and mitigate potential impacts on Earth's technology-reliant infrastructures. Future enhancements could include refining detection techniques and integrating additional solar observation data to further improve model accuracy and predictive capabilities.