A generalizable method for estimating meteor shower false positives

Abstract

The determination of meteor shower or parent body associations is inherently a statistical problem. Traditional methods, primarily the similarity discriminants, have limitations, particularly in handling the increasing volume and complexity of meteoroid orbit data. We introduce a new more statistically robust and generalizable method for estimating false positive detections in meteor shower identification, leveraging kernel density estimation (KDE). The method is applied to fireball data from the European Fireball Network, a comprehensive photographic fireball observation network established in 1963 for the detailed monitoring and analysis of fireballs across central Europe Utilizing a dataset of 824 fireballs observed by the European Fireball Network, we applied a multivariate Gaussian kernel within KDE and z-score data normalization. Our method analyzes the parameter space of meteoroid orbits and geocentric impact characteristics, focusing on four different similarity discriminants: $D_ SH $, $D'$, $D_ H $, and $D_ N The KDE methodology consistently converges toward a true established shower-associated fireball rate within the EFN dataset of 18-25<!PCT!> for all criteria. This indicates that the approach provides a more statistically robust estimate of the shower-associated component. Our findings highlight the potential of KDE combined with appropriate data normalization in enhancing the accuracy and reliability of meteor shower analysis. This method addresses the existing challenges posed by traditional similarity discriminants and offers a versatile solution adaptable to varying datasets and parameters.