Unsupervised clustering for collider physics
We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties. We show how this method can be applied to an unsupervised multiclass classification as well as for anomaly detection, which can be used for new physics searches.
PDF AbstractCategories
Data Analysis, Statistics and Probability
High Energy Physics - Experiment
