DATAFORGE: Automated Pipeline for Synthetically Generating Scientifically Accurate Datasets for Rare Event Model Training

In an era where increasingly sophisticated sensors are generating vast amounts of data, ranging from traditional DOD/IC networks to the accelerometers in phones and social media feeds, the Department of Defense (DOD) and Intelligence Community (IC) face a significant paradox. While the surge in data streams theoretically increases the chances of detecting rare yet critical threats—such as hypersonic missile flights or underground nuclear tests—the sheer volume is overwhelming human analysts. This creates a growing risk that even if a signature of interest is captured, it may go unnoticed. Moreover, the traditional machine learning models the DOD/IC have come to rely on are all ill-equipped to handle these infrequent events as there is a stark lack of real-world data for these events – resulting in a substantial risk to national security. Bascom Hunter’s DATAFORGE offers a transformative solution by using a physics-informed Generative Adversarial Network (GAN) to create scientifically accurate synthetic datasets from single instances of rare events. This automated pipeline allows analysts to validate and filter synthetic signatures, ensuring they meet stringent quality standards before they are used for model training. By embedding domain-specific knowledge directly into the GAN, DATAFORGE can simulate complex phenomena such as wave propagation and environmental noise, generating high-fidelity outputs that enhance Automated Target Recognition (ATR) capabilities. Our approach includes pre-processing, meticulous hyperparameter tuning, and robust post-processing, delivering a comprehensive toolset for the accurate detection of rare events. This not only addresses the immediate challenge of sparse data but also sets a new standard for generating high-quality, operationally relevant synthetic data, significantly enhancing the DOD's and IC's real-time sensor analysis capabilities.