Abstract
Sungrazing comets, known for their proximity to the Sun, are traditionally
classified into broad groups like Kreutz, Marsden, Kracht, Meyer, and non-group
comets. While existing methods successfully categorize these groups, finer
distinctions within the Kreutz subgroup remain a challenge. In this study, we
introduce an automated classification technique using the density-based spatial
clustering of applications with noise (DBSCAN) algorithm to categorize
sungrazing comets. Our method extends traditional classifications by finely
categorizing the Kreutz subgroup into four distinct subgroups based on a
comprehensive range of orbital parameters, providing critical insights into the
origins and dynamics of these comets. Corroborative analyses validate the
accuracy and effectiveness of our method, offering a more efficient framework
for understanding the categorization of sungrazing comets.
Funder
Chungnam National University
Publisher
The Korean Space Science Society
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