Speaker:
Jingjing Pan
Institution:
Yale
Date:
Tuesday, September 12, 2023
Time:
10:00 am
Location:
Virtual
Abstract:
Jets are collimated sprays of hadrons produced in the high energy collisions, the most ubiquitous yet challenging objects to optimally analyze at the LHC.
Modern machine learning (ML) techniques allow us to rethink how the high dimensional and increasingly subtle features of jets can be used to do physics in qualitatively new ways at the current energy reach.Powered by simulations, ML furthermore enables inference methods that can go much beyond a few dimensions by circumventing the traditional need for likelihood functions. In this talk, we present one of the first applications of OmniFold, a recently proposed ML-based unfolding method, to extending precision jet substructure measurements on ATLAS.
Host:
Daniel Whiteson