Transit Supply

Transit supply research encompasses the detection and analysis of transit events, primarily focusing on exoplanet detection and urban transportation systems. Current research leverages machine learning, particularly convolutional neural networks and ensemble methods, to improve detection accuracy in noisy data (e.g., exoplanet light curves) and analyze large datasets (e.g., social media feedback on transit systems). These advancements are significant for refining exoplanet characterization, improving transit system efficiency through user feedback analysis, and mitigating biases in existing detection methods.