Using Machine Learning to Get Serious about Systematics

by Daniel Whiteson (University of California Irvine (US))

Wednesday 26 Oct 2022, 16:00 → 17:30 Europe/Zurich

Abstract: A vital path to discovery of new physics lies through precision studies, where claims of significant deviations rely exquisitely on our understanding of systematic uncertainties. I will discuss two common practices in treatment of systematic uncertainties which rely on implicit assumptions that deserve revisiting and upgrades.  I will discuss how the use of machine learning can aid in the development of more robust systematic uncertainties.

 

Daniel Whiteson is a Professor of Physics at UC Irvine, and a member of the ATLAS experiment. He is interested in development of novel machine learning and statistical techniques to aid searches for new physics.
 

Systematics_whiteson_Oct2022.pdf

video1481730563.mp4