  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.
