Bridging the Sim-To-Real Gap with a Robust Deep Learning Framework for Extracting Rare Transient Anomalies in Complex Stochastic Time-Series

Detecting rare, morphologically diverse transient anomalies in data-scarce, highly stochastic time series remains a fundamental challenge in representation learning. Real-world temporal data often suffers from extreme class imbalance, variable sequence lengths, and significant domain shifts when relying on simulated training data. To address these challenges, we introduce a novel framework that leverages a Conditional Variational Autoencoder (CVAE) paired with a Power Spectral Density (PSD)-matched colored-noise padding strategy. This approach effectively bridges the simulation-to-real domain gap by synthesizing robust training representations that preserve underlying spectral characteristics. Furthermore, by treating temporal sequences as 1-dimensional images, our architecture employs a Convolutional Neural Network (CNN) to extract invariant features across multiple temporal windows via targeted trending and detrending mechanisms. We validate our framework on a highly complex real-world testbed: detecting anomalous X-ray time series events from Active Galactic Nuclei (AGN) driven by supermassive black holes. Our results demonstrate that this representation engineering approach significantly improves anomaly detection performance and generalization on highly stochastic datasets compared to standard sequence modeling baselines.