Distance metrics for dynamical association of meteors

Abstract

Meteor showers, originating from disruptions of comets or asteroids, offer insights into the composition and dynamics of the Solar System. However, distinguishing sporadic meteors from those belonging to specific meteoroid streams remains a challenge.This study evaluates four orbital similarity criteria within a five-dimensional parameter space (DSH, DD, DH, and ϱ2 [1-4]) for dynamical associations, using the CAMS database as a benchmark. Additionally, we assess various Machine Learning distance metrics with two vectors: ORBIT (based on heliocentric orbital elements) and GEO (based on geocentric observational parameters). We test the Top-k agreement and compute the optimal cut-offs for distinguishing sporadic events by analyzing the ROC curve using Youden’s J statistic