Astronomers are now faced with an overwhelming amount of data, with instruments like the Gaia satellite and the upcoming Rubin observatory generating billions of observations. As data complexity increases, machine learning plays a crucial role in exploratory data analysis, particularly in identifying anomalies such as fast radio bursts. The challenge lies not only in processing vast datasets but also in developing better theoretical models to interpret the findings.
