Jet-level Anomaly Detection in the ATLAS Y->XH Search with a Variational Recurrent Neural Network

We present the implementation of machine-learning based anomaly detection to a generic dijet resonance search with LHC proton collision data collected by the ATLAS Experiment. Specifically, we train over data with a novel variational recurrent neural network (VRNN) that identifies anomalous jets solely based on their inconsistency with the background only hypothesis. The VRNN produces a per-jet anomaly score, whose performance is evaluated across a wide variety of new physics topologies to ensure model-independence, across which a selection on the anomaly score is shown to yield between 5-30% increase in significance of signal over background. We also describe the first application of this method to ATLAS data by way of a search for generic new bosons Y and X in association with a Higgs boson. We have utilized the anomaly score to define a model-independent signal region in this analysis, marking the first use of fully unsupervised machine learning in an ATLAS physics search.