Tag: secular-resonances

This paper presents a major enhancement to the resonances Python package that now implements full support for identifying and analyzing secular resonances. Building upon the established mean-motion resonance framework, the implementation introduces: (1) a flexible mathematical expression parser supporting arbitrary combinations of fundamental frequencies (g, s, gi, si), enabling analysis of both linear resonances (v5, v6, v16) and more than 70 nonlinear resonances from the literature; (2) specialized libration detection algorithms optimized for secular timescales, with automated parameter adaptation for extended integration times; (3) integration with existing mean-motion resonance workflows through consistent interfaces, allowing unified dynamical studies. The package has been tested through automated unit and integration tests and manual validation against examples from the literature, with all test cases---including v6, v16, z1, z2, 2v6 - v5, and 3v6 - 2v5 resonances passed successfully (with minor exceptions). The new version maintains the simplicity of the original interface, requiring only 3--4 lines of code for standard analyses, while providing researchers with powerful tools for systematic dynamical analysis and asteroid family studies. The package is available on GitHub under the MIT license.

This study explores how well various machine learning classifiers can identify mean-motion resonances in the main belt using supervised learning. The most popular classifiers are assessed: k-Nearest Neighbours, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, and Naïve Bayes. In contrast to previous studies that often relied on default ML configurations, this research conducts a detailed investigation, fine-tuning, and testing of each classifier across various parameters. The results show that simpler models, especially k-Nearest Neighbours and Decision Tree, perform better than more complex ones, particularly in terms of F1 scores. The paper provides guides on selecting features, parameters, and training set sizes for optimal classifier performance and outlines a method for developing effective machine-learning models for asteroid classification.