Unsupervised Outlier Detection in Heavy-Ion Collisions

TL;DR

This paper explores unsupervised learning techniques, including PCA and Autoencoders, for detecting outliers in high energy nuclear collision data, emphasizing reconstruction error and dimensionality reduction.

Contribution

It introduces a framework using PCA and Autoencoders for outlier detection in nuclear collision data, highlighting the importance of reconstruction error and optimal dimensionality.

Findings

Reconstruction error effectively distinguishes outliers from background.

Optimal number of reduced dimensions improves detection performance.

Autoencoders outperform PCA in outlier detection accuracy.

Abstract

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). We find that mainly the reconstruction error is a good measure to distinguish outliers from background. The performance of the algorithms is compared using a ROC curve. It is shown that the number of reduced (encoded) dimensions to describe a single event contributes significantly to the performance of the outlier detection task. We find that the model which is best suited to separate outlier events requires a good performance in reconstructing events and at the same time a small number of parameters.