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arXiv 1506.04046astro-ph.SRastro-ph.EPphysics.data-anphysics.space-ph
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用自动CORIMP星表研究日冕物质抛射的动力学

Investigating the Kinematics of Coronal Mass Ejections with the Automated CORIMP Catalog

Jason P. Byrne

摘要:

在日冕物质抛射数据中研究日冕物质抛射(CME)可能具有挑战性,因为它们的扩散结构和瞬态性质,加上它们的动力学,形态和发生频率的变化。从太阳和日光层观测站(SOHO)等任务获得的大量数据使得CME的手动编目变得繁琐,并且容易出现人为错误,因此需要一种强大的检测和分析方法,并且通常是首选。一个新的冠状图像处理目录称为CORIMP已开发的努力实现这一目标,通过实施动态背景分离技术和多尺度边缘检测。这些算法一起隔离和CME结构的大角度光谱日冕仪(LASCO)的视场内的SOHO。CORIMP还将Savitzky-Golay滤波器以及二次和线性拟合应用于高度-时间测量,以更好地揭示整个天空平面上的真实CME速度和加速度剖面。在这里,我们提出了一个新的结果从CORIMP CME目录的样本,并直接将它们与其他自动化目录的计算机辅助CME跟踪(CACTus)和太阳爆发事件探测系统(SEEDS),以及手动CME目录在协调数据分析研讨会(CCED)数据中心和以前发表的研究样本事件。我们进一步研究了一种无监督机器学习的形式,通过使用k均值聚类算法来区分在空间和时间上靠近发生的多个CME的检测。虽然挑战仍然存在,但这种调查和结果比较表明了CORIMP目录的可靠性和鲁棒性,证明了其在整个LASCO数据集内检测和跟踪CME的有效性。

英文摘要:

Studying coronal mass ejections (CMEs) in coronagraph data can be challenging due to their diffuse structure and transient nature, compounded by the variations in their dynamics, morphology, and frequency of occurrence. The large amounts of data available from missions like the Solar and Heliospheric Observatory (SOHO) make manual cataloging of CMEs tedious and prone to human error, and so a robust method of detection and analysis is required and often preferred. A new coronal image processing catalog called CORIMP has been developed in an effort to achieve this, through the implementation of a dynamic background separation technique and multiscale edge detection. These algorithms together isolate and characterise CME structure in the field-of-view of the Large Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also applies a Savitzky-Golay filter, along with quadratic and linear fits, to the height-time measurements for better revealing the true CME speed and acceleration profiles across the plane-of-sky. Here we present a sample of new results from the CORIMP CME catalog, and directly compare them with the other automated catalogs of Computer Aided CME Tracking (CACTus) and Solar Eruptive Events Detection System (SEEDS), as well as the manual CME catalog at the Coordinated Data Analysis Workshop (CDAW) Data Center and a previously published study of the sample events. We further investigate a form of unsupervised machine learning by using a k-means clustering algorithm to distinguish detections of multiple CMEs that occur close together in space and time. While challenges still exist, this investigation and comparison of results demonstrates the reliability and robustness of the CORIMP catalog, proving its effectiveness at detecting and tracking CMEs throughout the LASCO dataset.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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