Automated Spectral Deconvolution and Source Classification for Faint Ultraviolet Galaxies via Iterative Bayesian Reconstruction and Spatial-Temporal Correlation Analysis

**Abstract:** This paper introduces a novel framework for improving the detection and classification of faint ultraviolet (UV) galaxies, a crucial step in understanding early galaxy formation and reionization. Leveraging advanced spectral deconvolution techniques combined with iterative Bayesian reconstruction and spatial-temporal correlation analysis, our system overcomes limitations of traditional methods plagued by blending and noise. By integrating high-resolution spectral data with spatial proximity and temporal evolution patterns, we achieve a significant increase in the accuracy and resolution of UV galaxy identification and classification, with immediate application to existing and future ground-based and space-based UV telescope datasets. The system is designed for high throughput and scalability, enabling rapid analysis of large cosmological surveys.