12-16 October 2020
Zoom Meeting
Europe/London timezone

HEP NN training

12 Oct 2020, 10:30
2h
Zoom Meeting

Zoom Meeting

Speaker

Gregor Ksieczka

Description

This session will introduce aspects of unsupervised (and weakly supervised) learning methods and demonstrate these concepts using concrete problems from particle physics. The availability of high-quality synthetic data from Monte Carlo (MC) simulation is a key ingredient for the success of particle physics. However, the production and storage of these MC simulations occupies a large fraction of computing resources of big experimental collaborations. We will introduce generative machine learning models such as generative adversarial networks (GANs) and autoencoders which promise a way to greatly speed-up simulation. Furthermore, we will explore the idea of unsupervised searches for anomalies as a novel way of data quality monitoring and potential discovery.

Presentation Materials