On the directional nature of the fall of celestial objects on the surface of Venus

Abstract

This study demonstrates the critical need for circular data analysis in interpreting meteor impact locations on Venus. Traditional linear methods need to be improved to capture the cyclic nature of these data. We can better identify patterns and trends using specialized circular data techniques, enhancing our understanding of the mechanisms behind meteor impacts. By employing specialized statistical techniques that capture the cyclic nature of angular data, our analysis included Watson’s test at a 0.05 significance level, showing that the Venus surface crater dataset’s location parameters and solar elevations conform to a Von Mises distribution, while daytime and nighttime solar elevations do not. Using Bayesian Information Criterion (BIC) scores, we also determine that a two-component mixture of Fisher–von Mises distributions best models the spatial distribution of impact craters. These findings enhance our understanding of meteor impact processes on Venus and highlight the advantages of applying maximum-entropy directional models to planetary science.