Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost

NASA’s space-based telescopes Kepler and Transiting Exoplanet Survey Satellite (TESS) have detected billions of potential planetary signatures, typically classified with Convolutional Neural Networks (CNNs). In this study, we introduce a hybrid model that combines deep learning, dimensionality reduction, decision trees, and diffusion models to distinguish planetary transits from astrophysical false positives and instrumental artifacts. Our model consists of three main components: (i) feature extraction using the CNN VGG19, (ii) dimensionality reduction through t-Distributed Stochastic Neighbor Embedding (t-SNE), and (iii) classification using Conditional Flow Matching (CFM) and XGBoost. We evaluated the model on two Kepler and one TESS datasets, achieving F1-scores of 98% and 100%, respectively. Our results demonstrate the effectiveness of VGG19 in extracting discriminative patterns from data, t-SNE in projecting features in a lower dimensional space where they can be most effectively classified, and CFM with XGBoost in enabling robust classification with minimal computational cost. This study highlights that a hybrid approach leveraging deep learning and dimensionality reduction allows one to achieve state-of-the-art performance in exoplanet detection while maintaining a low computational cost. Future work will explore the use of adaptive dimensionality reduction methods and the application to data from upcoming missions like the ESA’s PLATO mission.