LHC analysis-specific datasets with Generative Adversarial Networks
This work addresses the need for efficient simulation in particle physics experiments, potentially reducing reliance on centrally produced events, though it is incremental as it builds on existing GAN methods.
The paper tackles the problem of generating large amounts of analysis-specific simulated events for LHC physics at low computing cost using GANs, showing that including regression terms in the loss function leads to substantial performance improvements and faster convergence.
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four-momenta in $Z \to μμ$ events at the LHC. We highlight how use-case specific issues emerge when the distributions of the considered quantities exhibit particular features. We show how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator. We develop an objective criterion to assess the geenrator performance in a quantitative way. With further development, a generalization of this approach could substantially reduce the needed amount of centrally produced fully simulated events in large particle physics experiments.