标题： ARCANE——行星际日冕物质抛射的早期检测 显示英文标题

标题： ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

摘要： 星际日冕物质抛射（ICMEs）是空间天气扰动的主要驱动因素，对技术和人类活动构成风险。 在太阳风原位数据中自动检测ICMEs对于早期预警系统至关重要。 虽然已经提出了几种方法来识别时间序列数据中的这些结构，但稳健的实时检测仍然是一个重大挑战。 在本研究中，我们介绍了ARCANE——第一个专门设计用于在现实操作约束下对流式太阳风数据进行早期ICME检测的框架，能够在不观察完整结构的情况下实现事件识别。 我们的方法通过将基于机器学习的方法与基于阈值的基线进行比较，评估了检测模型的优势和局限性。 之前在科学数据上验证过的ResUNet++模型显著优于基线，在检测高影响事件方面表现尤为突出，同时在低影响情况下也保持了良好的性能。 值得注意的是，我们发现使用实时太阳风（RTSW）数据而不是高分辨率科学数据只会导致性能轻微下降。 尽管存在操作环境的挑战，我们的检测流程在仅处理事件数据的极小部分时，达到了0.37的F1分数，并且平均检测延迟为事件持续时间的24.5%。 随着更多数据的可用，性能显著提高。 这些结果标志着在自动化空间天气监测方面迈出了重要一步，并为增强的实时预报能力奠定了基础。 显示英文摘要

摘要： Interplanetary coronal mass ejections (ICMEs) are major drivers of space weather disturbances, posing risks to both technological infrastructure and human activities. Automatic detection of ICMEs in solar wind in situ data is essential for early warning systems. While several methods have been proposed to identify these structures in time series data, robust real-time detection remains a significant challenge. In this work, we present ARCANE - the first framework explicitly designed for early ICME detection in streaming solar wind data under realistic operational constraints, enabling event identification without requiring observation of the full structure. Our approach evaluates the strengths and limitations of detection models by comparing a machine learning-based method to a threshold-based baseline. The ResUNet++ model, previously validated on science data, significantly outperforms the baseline, particularly in detecting high-impact events, while retaining solid performance on lower-impact cases. Notably, we find that using real-time solar wind (RTSW) data instead of high-resolution science data leads to only minimal performance degradation. Despite the challenges of operational settings, our detection pipeline achieves an F1-Score of 0.37, with an average detection delay of 24.5% of the event's duration while processing only a minimal portion of the event data. As more data becomes available, the performance increases significantly. These results mark a substantial step forward in automated space weather monitoring and lay the groundwork for enhanced real-time forecasting capabilities.