Improving the Automated Coronal Jet Identification with U-NET

TL;DR

This paper introduces an automated neural network-based method for detecting coronal jets in solar observations, significantly improving accuracy and enabling real-time analysis, which advances solar physics research.

Contribution

The paper presents AJIA, a novel U-NET based algorithm that outperforms previous semi-automated methods in coronal jet detection accuracy and speed.

Findings

AJIA achieves a detection precision of 0.81, compared to 0.34 by previous methods.

AJIA enables fast, real-time identification of coronal jets from solar observations.

The method facilitates long-term and collective studies of solar activity cycles.

Abstract

Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection, flaring, instabilities, and plasma heating. Automated identification of off-limb coronal jets has been difficult due to their abundant nature, complex appearance, and relatively small size compared to other features in the corona. In this paper, we present an automated coronal jet identification algorithm (AJIA) that utilizes true and fake jets previously detected by a laborious semi-automated jet detection algorithm (SAJIA, Liu et al. 2023) as the input of an image segmentation neural network U-NET. It is found that AJIA could achieve a much higher (0.81) detecting precision than SAJIA (0.34), meanwhile giving the possibility of whether each pixel in an input image belongs to a jet. We demonstrate that with the aid of artificial neural networks, AJIA could enable fast, accurate, and real-time coronal jet identification from SDO/AIA 304 \AA observations, which are essential in studying the collective and long-term behavior of coronal jets and their relation with the solar activity cycles.