Machine Learning at Colliders
by
Sala de Conferências
Departamento de Física FCTUC
Machine Learning (ML) is nowadays a universal tool in Particle Physics, clearly leveraging the reach of experiments. Particle reconstruction and identification, and signal detection are examples of tasks where ML’s flexibility and performance led to a significant efficiency improvement. In this seminar, I will showcase the diverse ML algorithmic approach with a survey of applications in the field of collider physics. ML will also have an influential role in the future of Particle Physics, namely in the challenging High-Luminosity LHC phase. Among others, R&D is seeking alternatives to ease the burden of large data set simulation by Monte Carlo and to extend the generality of Searches for New Physics with Anomaly Detection. In this context, I will present our exploratory study on the use of ML for Anomaly Detection in HEP.
Filipe Veloso e Pedro Costa
