Meteor Shower Detection with Density-Based Clustering

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.