Detection Limit

Detection limit, the smallest amount of a signal that can be reliably distinguished from background noise, is a critical parameter across diverse scientific fields. Current research focuses on improving detection limits through advanced signal processing techniques, including deep learning models like convolutional neural networks and attention-based architectures, often coupled with novel data pre-processing methods to enhance signal-to-noise ratios. These advancements are significantly impacting various applications, from improving the sensitivity of medical imaging and exoplanet detection to enhancing the accuracy of object recognition in autonomous systems and small target identification in defense applications. Optimizing detection limits remains a key challenge, with recent work emphasizing the trade-off between accuracy and minimizing uncertainty in quantification.