Exoplanet Candidate

Exoplanet candidate research focuses on identifying and characterizing planets orbiting stars other than our Sun. Current efforts heavily utilize machine learning, employing diverse architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and ensemble methods to analyze vast datasets from space telescopes like Kepler and TESS, as well as high-contrast imaging data. These techniques aim to improve detection sensitivity, reduce false positives, and efficiently extract atmospheric and orbital parameters, accelerating the pace of exoplanet discovery and characterization. This work is crucial for advancing our understanding of planetary system formation and the prevalence of potentially habitable worlds.