Application of machine learning to find anomalous events in the LZ data

LUX-ZEPLIN (LZ) is a dark matter direct detection experiment using a dual-phase xenon time projection chamber with a 7-ton active volume, expecting science results in 2022. Anomalies are expected in data in the early stages of the experiment, such as from misclassification of pulses and interaction types, as well as detector pathologies. Dimensional reduction is an unsupervised Machine Learning technique that can effectively identify anomalous events. High-dimensional data can be mapped with minimal loss of structure to a low-dimensional space, where the data can be visualized and clustered. In this presentation, we will discuss the application of the uniform manifold approximation and projection dimensional reduction algorithm to reduce the early LZ data to a 2D space for the identification of similar populations and anomalous events.