Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer

Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

Note:

22 pages, 9 figures

principal component analysis
neural network
numerical calculations: Monte Carlo
p p: scattering
top: pair production
electron positron: scattering
lepton: pair production
electron positron --> lepton antilepton
p p --> top anti-top

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