Jet-Images -- Deep Learning Edition
This work addresses the challenge of particle identification in high-energy physics, offering a general method to enhance sensitivity for discovering new particles and forces, though it is incremental as it builds on existing jet image concepts.
The paper tackled the problem of identifying highly boosted W bosons in particle physics by applying deep learning to jet images, achieving performance that surpasses standard physically-motivated feature-driven approaches.
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.