Automated Discovery of Jet Substructure Analyses

Understand

The study of the substructure of collimated particles from quarks and gluons, or jets, has the promise to reveal the details how color charges interact with the QCD plasma medium created in colliders such as RHIC and the LHC.

• Traditional jet substructure observables have been constructed using expert knowledge, and are largely transplanted, unmodified, from the high-energy physics, where the goal is primarily the study of boosted hadronic decays.
• A novel neural network architecture is described that is capable of examining theoretical models, and constructs, on its own, an analysis procedure that is sensitive to the internal model features.
• This architecture, in combination with symbolic regression, further allows the extraction of closed-form algebraic expressions from the learned result -- enabling the automatically constructed jet substructure analysis to be subsequently understood and reproduced by humans.