In the coming years, the LHC experiments will need to produce an incredible amount of Monte Carlo events to match the expected increase in luminosity. This challenge, together with the need of using more sophisticated generators, will stretch the computing resources and may limit the physics reach of the experiments.
In our paper, we provide a solution to overcome these problems by using Machine Learning techniques, specifically Generative Adversarial Networks (GANs). In this first attempt, we focussed on the relatively simple di-jet events but we also prepared the tools to produce more complex events such as top quark pair events and multi boson events which are produced in large quantities at LHC.
We actually trained two networks, one using the output of the generators (which is commonly called particle level) and another after the detector simulation and reconstructions (also known as reconstruction or detector level). The good agreement of first network with the training sample demonstrates that it is possible to use a relatively small number of events to train a GAN which can be subsequently be used to significantly increase the number of events as we are able to to generate 1 million events in less than a minute. The good agreement of second network demonstrate that the detector response and even the reconstruction steps can be reproduced by a GAN.
The results we obtained are a clear indication that GANs can have a high impact on LHC experiment in several areas. You can find more details on the paper but we would like to share a video showing the learning of the GAN as it is trained which cannot be included in the paper.
Let us know what you think of this new application of the GAN in the comments below or by contacting the authors of the paper.
Michele and Serena