Particle Identification plays a crucial role within the GlueX Barrel Calorimeter. This paper details the implementation of a novel Machine Learning architecture, which combines cutting-edge conditional generative models with clustering algorithms, capable of extracting both e cient and high purity data samples, while only relying on information from only one type of sample. We demonstrate the validity of our approach and highlight its use as a neutron detection device, emphasizing its ability to limit assumptions on background samples. This architecture is flexible and can be extended to multiple categories. Remarkably it can be deployed for a wide range of problems, e.g., anomaly detection and data quality control.