AI-driven Classification of Interstellar Medium Morphologies: From Big Data to Physical Insights

Abstract

The interstellar medium is a multi-phase material that fills the space between the stars with densities and temperatures covering over 6 orders of magnitude. Since of the exponential growth of observational data from facilities like the Atacama Large Millimeter/submillimeter Array and Herschel Space Observatory, the traditional morphological classification methods suffer from this data bursting. Artificial intelligence has emerged as a transformative tool to address this challenge, enabling efficient, objective classification of interstellar medium morphologies from high-dimensional, large-volume datasets. This paper analyze the advances in AI-driven interstellar medium morphology classification, including the research background and limitations of traditional methods, analysis of interstellar medium big data characteristics and processing challenges. Then, the author detail the commonly used AI methods, including unsupervised clustering, supervised Deep Learning, and physics-informed machine learning, and their benefits in interstellar medium research.