Figuring Out Interstellar Objects Using Memory Based Learning Approaches

The classification and cataloging of interstellar objects and phenomena constitutes a fundamental task in the fields of astronomy and astrophysics. Traditionally, these types of classifications have been based on expert knowledge and manual analysis of observational data, which can be time consuming and prone to biases. Our study presents a new approach to classify interstellar objects using memory-based learning techniques This method involves building a comprehensive database, where each entry corresponds to a unique interstellar object, enriched with relevant observational features. The performance of the memory-based classifier is compared with traditional manual classification methods and other machine learning algorithms. This interpretability is especially useful in astronomy, where discoveries and scientific advances can result from knowing, why an object is categorized in a particular way. This work advances the field of interstellar object classification by putting forth a memory-based learning strategy that increases process efficiency and automates the task.