Speaker
Description
In the recent years, high energy physics discoveries have been driven by the increasing of detector volume and/or granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process results with current methods and algorithms. Graph neural networks, particularly graph convolution networks, have been shown to be powerful tools to address these challenges. These methods however raise some difficulties with their computing resource needs. In particular, representing physics events as graphs is a tricky problem that demands a good balance between resource consumption and graph quality, which can greatly affects the accuracy of the model.
We propose a graph convolution network pipeline architecture to perform classification and regression tasks on calorimeter events and discuss its performances. It is designed for resource constrained environments, and in particular to efficiently represent calorimeter events as graphs, allowing up to a quadratic improvement in complexity with satisfying accuracy. Finally, we discuss possible applications to other high energy physics detectors.
