Séminaires

Recent advances on applying Machine Learning to High Energy Physics

par David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)

Europe/Paris
Description

Use of Machine Learning in High Energy Physics is growing exponentially. Based in particular on the October 2020 Interexperiment Machine Learning workshop at CERN, I shall report on most recent advances in particular Generative Adversarial Networks to accelerate detector simulation, Invertible Neural Networks and Normalising Flows, anomaly detection for automatic data quality monitoring (and maybe new physics ?), and Graph Neural Networks applications.

https://cern.zoom.us/j/3489975764?pwd=MmtSYXgzWDI3QVFqaGR6NWlWaU1xdz09