Anomaly Detection: Tips, Tricks and Examples on How to Find Outlying Events in the Standard Model Using Machine Learning

How many phase spaces in the Standard Model have not been analyzed, let alone thought of? In the past few years, interest has grown rapidly in using machine learning for anomaly detection in High Energy Physics in both the ATLAS and CMS experiment located at CERN Large Hadron Collider. This approach helps define anomalous phase spaces within the Standard Model (SM) that may contain Beyond the Standard Model (BSM) events. Several approaches have been developed and successfully applied to find these anomalous phase spaces. How are these techniques developed? What metrics can be used to define an effective phase space? Can these techniques increase discovery sensitivity? In this presentation, I will explore various BSM model agnostic approaches, discuss results using example BSM models such as the radion and dark-matter, and offer practical insights on formulating an Anomaly Detection technique.