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With the great promise of deep learning, discoveries of new particles at theLarge Hadron Collider (LHC) may be imminent. Following the discovery of a newBeyond the Standard model particle in an all-hadronic channel, deep learningcan also be used to identify its quantum numbers. Convolutional neural networks(CNNs) using jet-images can significantly improve upon existing techniques toidentify the quantum chromodynamic (QCD) (`color') as well as the spin of atwo-prong resonance using its substructure. Additionally, jet-images are usefulin determining what information in the jet radiation pattern is useful forclassification, which could inspire future taggers. These techniques improvethe categorization of new particles and are an important addition to thegrowing jet substructure toolkit, for searches and measurements at the LHC nowand in the future.

Identifying the Quantum Properties of Hadronic Resonances using Machine Learning