Meteor Shower Detection with Density-Based Clustering

TL;DR

This paper introduces a novel meteor shower detection method using the DBSCAN clustering algorithm, effectively identifying known showers from large all-sky camera datasets with quantified reliability.

Contribution

The paper applies DBSCAN to meteor data, incorporating measurement errors and validation, providing a robust, scalable approach for meteor shower detection.

Findings

Detected 25 strong and 6 weak meteor showers matching known showers

Validated method with false positive/negative analysis

Compared results successfully with existing algorithms

Abstract

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.