Solar Transient Recognition Using Deep Learning (STRUDL) for heliospheric imager data

TL;DR

This paper introduces STRUDL, a deep learning model for automated detection and segmentation of Coronal Mass Ejections in heliospheric imager data, aiming to improve space weather forecasting.

Contribution

The paper presents a novel deep learning approach for automatic CME detection and segmentation in HI data, enhancing current manual and semi-automated methods.

Findings

STRUDL effectively detects CME fronts in HI data.

The model demonstrates potential for automated CME tracking.

Challenges remain in segmenting faint and interacting CMEs.

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 towards 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.