Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA.

Calibration of the Top-Quark Monte Carlo Mass.

We present a method to establish, experimentally, the relation between the top-quark mass m_{t}^{MC} as implemented in Monte Carlo generators and the Lagrangian mass parameter m_{t} in a theoretically well-defined renormalization scheme. We propose a simultaneous fit of m_{t}^{MC} and an observable sensitive to m_{t}, which does not rely on any prior assumptions about the relation between m_{t} and m_{t}^{MC}. The measured observable is independent of m_{t}^{MC} and can be used subsequently for a determination of m_{t}.