Jet Classification

Jet classification focuses on distinguishing different types of jets—collimated sprays of particles—produced in high-energy physics experiments, primarily to identify rare events indicative of new physics. Current research heavily utilizes deep learning, including deep neural networks, vision transformers, and graph neural networks, often applied to jet images or particle sets, to improve classification accuracy and efficiency. These advancements are crucial for analyzing the massive datasets generated by experiments like the Large Hadron Collider, enabling more precise measurements and potentially revealing new fundamental particles or interactions. Furthermore, similar techniques are finding applications in other fields, such as leak detection in pipelines, showcasing the broader impact of jet classification methodologies.