15-18 April 2019
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production

17 Apr 2019, 11:25
20m
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium

CERN

400
Show room on map

Speaker

Mr Saul Alonso Monsalve (CERN)

Description

We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. The best match parameter values that produce the most accurate simulated images can be extracted and used to re-tune the default simulation to minimise any bias when applying image recognition techniques to simulated and true images. In the case of a real-world experiment, the true data is replaced by experimental data with unknown true parameter values. The Model-Assisted Generative Adversarial Network uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast image production.

Preferred contribution length 20 minutes

Primary authors

Mr Saul Alonso Monsalve (CERN) Leigh Howard Whitehead (University of Cambridge (GB))

Presentation Materials