Issue |
A&A
Volume 686, June 2024
|
|
---|---|---|
Article Number | A130 | |
Number of page(s) | 8 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202349024 | |
Published online | 05 June 2024 |
A generalizable method for estimating meteor shower false positives★
Institut Mécanique Céleste et de Calcul des Éphémérides, Observatoire de Paris, PSL,
75014
Paris,
France
e-mail: patrick.shober@obspm.fr
Received:
19
December
2023
Accepted:
29
February
2024
Context. 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.
Aims. 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
Methods. 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: DSH, D′, DH, and DN.
Results. The KDE methodology consistently converges toward a true established shower-associated fireball rate within the EFN dataset of 18–25% for all criteria. This indicates that the approach provides a more statistically robust estimate of the shower-associated component.
Conclusions. 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.
Key words: methods: data analysis / methods: statistical / comets: general / meteorites, meteors, meteoroids / minor planets, asteroids: general
The EFN fireball data used in this study is available in the publication by Borovička et al. (2022). The code developed for this study is available at https://zenodo.org/records/10406556.
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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