arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1702.02656astro-ph.EP
收藏

基于密度聚类的流星雨探测

Meteor Shower Detection with Density-Based Clustering

Glenn Sugar, Althea Moorhead, Peter Brown, Bill Cooke

摘要:

我们提出了一种新的方法来检测流星雨使用基于密度的空间聚类算法的应用程序与噪声(DBSCAN; Ester等。1996)。DBSCAN是一种现代的簇检测算法,非常适合从全天空相机数据中提取流星雨的问题,因为它能够有效地从大型数据集中提取不同形状和大小的簇。我们应用此流星雨检测算法的数据集,其中包含25,885流星轨迹和轨道从美国宇航局的全天火球网络和南安大略省流星网络(SOMN)。使用基于太阳经度,地心速度和日心黄道辐射的距离度量,我们在数据中发现25个强集群检测和6个弱检测,所有这些都与已知的阵雨很好地匹配。我们在分析中包括测量误差,以量化集群发生的可靠性和每个流星属于给定集群的概率。我们验证我们的方法,通过假阳性/阴性分析,并与一个既定的淋浴检测算法的比较。

英文摘要:

We present a new method to detect meteor showers using the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN; Ester et al. 1996). DBSCAN is a modern cluster detection algorithm that is well suited to the problem of extracting meteor showers from all-sky camera data because of its ability to efficiently extract clusters of different shapes and sizes from large datasets. We apply this shower detection algorithm on a dataset that contains 25,885 meteor trajectories and orbits obtained from the NASA All-Sky Fireball Network and the Southern Ontario Meteor Network (SOMN). Using a distance metric based on solar longitude, geocentric velocity, and Sun-centered ecliptic radiant, we find 25 strong cluster detections and 6 weak detections in the data, all of which are good matches to known showers. We include measurement errors in our analysis to quantify the reliability of cluster occurrence and the probability that each meteor belongs to a given cluster. We validate our method through false positive/negative analysis and with a comparison to an established shower detection algorithm.

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。

↑