Abstract: Machine learning in high-energy particle physics experiments, from simulation, through reconstruction to physics analysis
Machine learning has become ubiquitous in high-energy experimental physics, transforming almost every aspect of the software. I will provide an overview of how machine learning is used in the ATLAS experiment at the Large Hadron Collider and illustrate this with selected examples including the simulation of the detector response, the reconstruction of the raw data and in physics analysis. Perspectives about where machine learning might be used in the future in ATLAS will also be provided.
