On the autonomous detection of coronal mass ejections in heliospheric imager data
On the autonomous detection of coronal mass ejections in heliospheric imager data
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日光层成像仪数据中日冕物质抛射的自主检测
DOI：
10.1029/2011ja017439
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发表时间：
2012
影响因子：
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通讯作者：
C. E. Burns
中科院分区：
文献类型：
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作者：
S. Tappin;T. Howard;M. M. Hampson;Robin N. Thompson;C. E. Burns
我们报道了用于太阳质量抛射成像仪（SMEI）的日冕物质抛射（CME）自动检测工具（AICMED）的开发。利用日球成像仪观测到的日冕物质抛射比日冕仪观测到的日冕物质抛射要困难得多，因为日冕物质抛射与背景光的对比度较低，强度变化范围较大，而且容易与其他瞬变活动混淆。在SMEI图像中，日冕物质抛射在明亮多变的背景中表现为非常微弱的碎片弧。AICMED的工作方式与计算机辅助CME跟踪（CACTus）相同，使用延长时间j -map的霍夫变换从数据集中提取直线。我们将AICMED的结果与人工测量的cme数据进行比较，这些数据来自SMEI早期操作的近三年数据。AICMED确定了83个可验证的事件。在这46个可以与人工识别的事件相匹配的事件中，大多数未检测到的事件可以解释。其余37个AICMED事件是新发现的日冕物质抛射。错误识别的比例很高，在自主检测事件中占71%。我们发现AICMED作为感兴趣区域的亮点非常有效，并且是自主日球成像仪CME检测的有希望的第一步，但SMEI数据太嘈杂，该工具无法完全自动化。
[1] We report on the development of an Automatic Coronal Mass Ejection (CME) Detection tool (AICMED) for the Solar Mass Ejection Imager (SMEI). CMEs observed with heliospheric imagers are much more difficult to detect than those observed by coronagraphs as they have a lower contrast compared with the background light, have a larger range of intensity variation and are easily confused with other transient activity. CMEs appear in SMEI images as very faint often-fragmented arcs amongst a much brighter and often variable background. AICMED operates along the same lines as Computer Aided CME Tracking (CACTus), using the Hough Transform on elongation-time J-maps to extract straight lines from the data set. We compare AICMED results with manually measured CMEs on almost three years of data from early in SMEI operations. AICMED identified 83 verifiable events. Of these 46 could be matched with manually identified events, the majority of the non-detections can be explained. The remaining 37 AICMED events were newly discovered CMEs. The proportion of false identification was high, at 71% of the autonomously detected events. We find that AICMED is very effective as a region of interest highlighter, and is a promising first step in autonomous heliospheric imager CME detection, but the SMEI data are too noisy for the tool to be completely automated.