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.
Abstract (translated)
在太空探索的时代，太空天气的影响越来越明显。这一现象的核心是太阳黑子现象，黑子对卫星和飞机的运行具有重要影响。这些黑子分布在太阳上，其特征是开放式的磁场线和相对较冷的温度，导致太阳风以更高的速率发射。为了预测这些黑子对地球的影响，我们的研究利用计算机视觉技术确定黑子区域，并使用太阳能动力学观测站（SDO）的图像估计它们的尺寸。此外，我们运用深度学习方法，特别是长短时记忆（LSTM）方法，对黑子区域的数据进行分析和预测，预测它们在七个不同太阳区域中的尺寸。通过评估黑子区域的时间序列数据，我们的研究旨在揭示黑子行为的模式，并理解它们对太空天气事件的影响。这次调查标志着我们向前迈进，提高我们对预测和应对太空天气事件的准备能力，从而影响地球及其技术设备的未来。
URL
https://arxiv.org/abs/2301.06732
