Detector Response

Detector response characterization aims to accurately model how detectors translate physical phenomena into measurable signals, crucial for data analysis in diverse fields like astrophysics and particle physics. Current research heavily utilizes machine learning, employing neural networks (including recurrent, generative adversarial, and autoencoders) to model complex, often non-linear, detector responses and improve signal processing, particularly in high-resolution or high-background environments. These advancements enable more precise data analysis, leading to improved event reconstruction, background rejection, and ultimately, more accurate scientific inferences.