Boost invariant polynomials for efficient jet tagging
Jose M. Muñoz, Ilyes Batatia, Christoph Ortner
Abstract
Abstract Given the vast amounts of data generated by modern particle detectors, computational efficiency is essential for many data-analysis jobs in high-energy physics. We develop a new class of physically interpretable boost invariant polynomial (BIP) features for jet tagging that achieves such efficiency. We show that, for both supervised and unsupervised tasks, integrating BIPs with conventional classification techniques leads to models achieving high accuracy on jet tagging benchmarks while being orders of magnitudes faster to train and evaluate than contemporary deep learning systems.
Topics & Concepts
InterpretabilityInvariant (physics)Computer scienceJet (fluid)Artificial intelligenceRepresentation (politics)Particle physicsTheoretical computer scienceMachine learningPhysicsQuantum mechanicsLawThermodynamicsPolitical sciencePoliticsParticle physics theoretical and experimental studiesComputational Physics and Python ApplicationsParticle Detector Development and Performance