Unsupervised Learning Techniques for Identification of Anomalous LZ Waveform Data

LUX-ZEPLIN (LZ) is a large-scale dark matter direct detection experiment that employs a time projection chamber (TPC) to observe particle interactions recorded as waveforms. In this work, we explore how unsupervised machine learning applied to waveforms can be used to characterize these interactions, with the goal of identifying anomalous events and detector pathologies. We introduce a framework for analyzing waveform shapes using dimensionality reduction. Applying this approach to single-scatter data, we cluster waveforms in the latent space constructed without explicit labels. The resulting regions in the embedding appear correlated with physically meaningful features, such as the identification of unphysical drift time events, a proxy for accidental coincidence events, with high recall (87%).