Graph Neural Network Architectures for Fast Simulation and Muon Momentum Inference at the CMS Detector

We explore the potential of graph neural networks in various applications in high energy physics including fast simulation of boosted jets and muon momentum estimation in the CMS detector. Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating the particle showers and interactions in the detector is both time consuming and computationally expensive. Classical fast simulation approaches based on non-parametric approaches can improve the speed of the full simulation but suffer from lower levels of fidelity. For this reason, alternative methods based on machine learning can provide faster solutions, while maintaining a high level of fidelity. The main goal of a fast simulator is to map the events from the generation level directly to the reconstruction level. We introduce a graph neural network-based autoencoder model that provides effective reconstruction of calorimeter deposits using the earth mover distance metric. On the other hand, we propose to use graph networks to infer the momentum of muons in the Cathode Strip Chambers given their ability to account for the several features affecting the particles' trajectories.