Speaker
Description
In the realm of high-energy physics, the use of graph network-based implementations offers the advantage of handling input datasets more closely aligned with their collection process in collider experiments. GNN-based approaches address the graph anomaly detection problem by utilizing information about graph features and structures to effectively learn to score anomalies. We represent a single jet as a graph, with each node corresponding to a hadronic constituent clustered into the jet. This approach enables the identification of anomalous jets, contributing to the detection of anomalies at the jet level.
We use simulated datasets of Dark Jets events as the benchmark signal model, where a heavy vector boson Z′ mediator connects a Standard Model (SM) quark pair with a pair of dark quarks. These dark quarks shower and hadronize, generating dark jets. For the background, we consider QCD dijet events.
Our goal is to extract a vector embedding that maps high-dimensional graph information into a low-dimensional vector using convolution and pooling mechanisms. These mechanisms efficiently propagate and aggregate information across the graph. The resulting vector embedding serves as input to an AD method, such as a one-class Deep Support Vector Data Description (DeepSVDD) and Autoencoders, allowing for the prediction and classification of jets based on their anomaly scores. We compare the performance of these models with baseline deep learning approaches.
| Track | Anomaly detection |
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