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Monte Carlo Integration and Generation with Neural Nets

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If you have a question about this talk, please contact Francesco Coradeschi .

The general problem of Monte Carlo integration and event generation in physics is to produce a sample of points which are distributed over phase space according to some differential cross section. I will discuss a framework in which an artificial neural network can be trained to perform this task. This can be viewed as a generalization of standard MC techniques, such as the VEGAS algorithm. I will present the considerations that go into the architecture of the neural net, and show results obtained for a number of simple processes of relevance to particle physics.

This talk is part of the HEP phenomenology joint Cavendish-DAMTP seminar series.

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