Particle Flow

Particle flow algorithms reconstruct the individual particles produced in high-energy physics collisions by combining information from different detector subsystems, aiming for improved accuracy and efficiency in event reconstruction. Current research heavily utilizes machine learning, particularly graph neural networks, to replace or augment traditional rule-based methods, focusing on optimizing model performance and interpretability through techniques like layerwise relevance propagation. These advancements are crucial for analyzing data from current and future colliders, leading to more precise measurements of fundamental particles and their interactions, and enabling applications in diverse fields like passive surveillance and marine mammal research.