Author Notes
Categorizing variable stars can reveal information ranging from stellar properties to cosmic distances. Traditionally, the classification process relied on human expertise and limited data. With the emergence of transformative machine learning methodologies and large-scale cosmological surveys, we are able to perform more efficient, accurate, and robust handling of expansive databases. This review traces the evolution of the classification of variable stars and highlights advancements from the latest surveys and cutting-edge technology. Reviewing papers in the field provides new insights into potential directions for improving classification methods.
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