Solar Transient Recognition Using Deep Learning (STRUDL) for Heliospheric Imager Data

Abstract Coronal Mass Ejections (CMEs) are space weather phenomena capable of causing significant disruptions to both space‐ and ground‐based infrastructure. The timely and accurate detection and prediction of CMEs is a crucial steps toward implementing strategies to minimize the impacts of such events. CMEs are commonly observed using coronagraphs and heliospheric imagers (HIs), with some forecasting methods relying on manually tracking CMEs across successive images in order to provide an estimate of their arrival time and speed. This process is time‐consuming and results may exhibiting considerable interpersonal variation. We investigate the application of machine learning (ML) techniques to the problem of automated CME detection, focusing on data from the HI instruments aboard the STEREO spacecraft. HI data facilitates the tracking of CMEs through interplanetary space, providing valuable information on their evolution. Building on advances in image segmentation, we present the Solar Transient Recognition Using Deep Learning (STRUDL) model. STRUDL is designed to automatically detect and segment CME fronts in HI data. We address the challenges inherent to this task and evaluate the model's performance across a range of solar activity conditions. To complement segmentation, we implement a basic tracking algorithm that links CME detections across successive frames, thus allowing us to automatically generate time‐distance profiles. Our results demonstrate the feasibility of applying ML‐based segmentation techniques to HI data, while highlighting areas for future improvement, particularly regarding the accurate segmentation and tracking of faint and interacting CMEs.