Unsupervised Outlier Detection in Heavy-Ion Collisions

Understand

We present different methods of unsupervised learning which can be used for outlier detection in high energy nuclear collisions.

• The UrQMD model is used to generate the bulk background of events as well as different variants of outlier events which may result from misidentified centrality or detector malfunctions.
• The methods presented here can be generalized to different and novel physics effects.
• To detect the outliers, dimensional reduction algorithms are implemented, specifically the Principle Component Analysis (PCA) and Autoencoders (AEN).