Automated Exoplanet Transit Light Curve Processing Pipeline

This paper presents an automated pipeline for processing exoplanet transit light curves with improved denoising and quality classification capabilities. Our machine learning approach combines wavelet denoising, outlier detection, and random forest classification to automatically identify high quality transit signals while rejecting systematic artifacts. Testing on synthetic and archival datasets demonstrates significant improvements in signal-to-noise ratio and detection completeness compared to traditional methods.