Speaker
Simon Rothman
(Massachusetts Inst. of Technology (US))
Description
Many high-energy physics analyses rely on various machine learning models for both event reconstruction and signal/background discrimination. One of the major sources of systematic uncertainty in these analyses is due to residual mismodelling in the detailed simulation samples used to train these algorithms. In this work, we will discuss a novel approach to correcting for these systematic effects using contrastive learning and demonstrate some preliminary results.
Authors
Dylan Sheldon Rankin
(Massachusetts Inst. of Technology (US))
Philip Coleman Harris
(Massachusetts Inst. of Technology (US))
Simon Rothman
(Massachusetts Inst. of Technology (US))
