Deep Learning

This paper presents an algorithm based on Graph Neural Network for charged-particle track reconstruction in the ATLAS Inner Tracker. Using realistic simulation data, we demonstrate the performance of the algorithm in comparison with the state-of-the-art technique.

We explore the use of conditional normalizing flow in the simulation of interaction between hadronic particles and atomic nuclei in ordinary matter. We trained generative models to reproduce data simulated by the state-of-the-art simulator, conditioned on the kinematics of the incoming hadron.