Tag: computing

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.

Classical machine learning has been actively utilized in astronomy to address various challenges, including predicting orbital stability, classifying asteroids, galaxies, and other objects, and analyzing images. However, the emerging trend in artificial intelligence involves the use of large language models such as GPT-4 and ChatGPT. These models are trained on a large corpus of text and can perform a wide range of natural language processing tasks, including text generation, translation, summarization, and classification. Surprisingly, these capabilities present significant potential for application in astronomy. This paper demonstrates how the new model gpt-4-vision-preview can analyze visual patterns and accurately classify asteroids as resonant or nonresonant with high accuracy. This process requires no training, fine-tuning, or coding beyond writing the appropriate prompt in natural language. Moreover, this approach can be extended to other common problems within astronomy.

In this paper, a new open-source package ‘resonances’ written in python is introduced. It allows to find, analyse, and plot two-body and three-body mean-motion eccentricity-type resonances in the Solar and other planetary systems. The package has a better accuracy of the automatic identification procedure for resonant objects compared to previous studies. Furthermore, it has built-in integrations with AstDyS and NASA JPL catalogues. The code is extensively documented and tested with automatic tests. The package is available on GitHub under MIT Licence.