Detector Design Optimization

Detector design optimization focuses on improving the performance and robustness of detectors across diverse applications, from medical imaging to high-energy physics and computer vision. Current research emphasizes leveraging machine learning techniques, including gradient estimation methods, deep unfolding architectures like the Hubbard-Stratonovich detector, and boosted learning with residual physics models, to enhance detector sensitivity, resolution, and resilience to adversarial attacks. These advancements are crucial for improving the accuracy and reliability of various technologies, ranging from medical diagnostics and communication systems to autonomous vehicles and security systems.