Speaker
Description
Data analyses in the high-energy particle physics (HEP) community more and more often exploit advanced multivariate methods to separate signal from background processes. In this talk, a maximally unbiased, in-depth comparison of the graph neural network (GNN) architecture, which is of increasing popularity in the HEP community, with the already well-established technology of fully connected feed-forward deep neural networks (DNNs) is presented. When it comes to choosing a suitable machine-learning model, it is not a priori clear, what model this should be to benefit from inherent properties of the task. Also, the design of a fair and unbiased benchmark is non-trivial. This GNN vs. DNN comparison is insightful in terms of the details it reveals as to which aspects of GNNs are superior to DNNs - and which are not. The study is performed on a typical data set of a complex challenge recently faced at the Large Hadron Collider: the classification of events with top quark-antiquark pairs with additional heavy flavour jets originating from gluon splittings, Z or Higgs bosons.
The study is documented in the paper “A Case Study of Sending Graph Neural Networks Back to the Test Bench for Applications in High‐Energy Particle Physics” published in Computing and Software for Big Science, https://doi.org/10.1007/s41781-024-00122-3.
| Primary Field of Research | Machine Learning |
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