VIRTUAL AI/Machine Learning Seminar: Measuring QCD Splittings with Invertible Networks

  • Feb. 25, 2021, 11:00 am US/Central

Speaker: Theo Heimel, Heidelberg University

Abstract: QCD splittings are among the most fundamental theory concepts at the LHC. In this talk, I will present how conditional invertible neural networks, a realization of normalizing flows, can be used to extract posterior distributions for QCD theory parameters from low-level jet observables. This approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties. Starting with jets from a toy parton shower generator, I will discuss the effect of the full shower, hadronization, and detector effects.