Particle Reconstruction

Particle reconstruction aims to identify and characterize individual particles from complex detector signals in experiments across various fields, from high-energy physics to cryo-electron microscopy. Current research heavily utilizes deep learning, particularly graph neural networks and convolutional neural networks, to improve reconstruction accuracy and efficiency, often incorporating techniques like super-resolution and self-supervised learning to handle noisy data and complex particle interactions. These advancements lead to more precise measurements of particle properties and improved understanding of underlying physical processes, impacting fields ranging from fundamental physics to structural biology.