Speaker
Rikab Gambhir
(Massachusetts Institute of Technology)
Description
Many Machine Learning (ML)-based Anomaly Detection (AD) methods are based on "extended bump hunt", where ML cuts are used to enhance the significance of signal in a bump. We show how, by taking advantage of the fact that ML methods (e.g. CATHODE with CWoLa) often involve learning the likelihood ratio of the signal and background hypotheses, events can be weighted rather than cut for improved (and in some cases, optimal) sensitivity. Moreover, this removes the need to manually select cuts or working points. We demonstrate this improvement in using CATHODE to find the Upsilon in CMS Open Data, where likelihood reweighting improves the ordinary sensitivity from 5.7σ to 6.4σ. THIS TALK IS THE SECOND OF TWO PARTS.
Primary author
Rikab Gambhir
(Massachusetts Institute of Technology)
