Boosting high energy physics with generative networks

Physicists at the Large Hadron Collider (LHC) are searching for signs of new physics to answer fundamental questions like the nature of dark matter. In many scenarios these signals are expected to be as rare as 1 in 10^10 events. We therefore require simulations which can model complex event structures with high precision. LHC physics is unique in the sense that we can rely on first-principles based predictions, which means that simulations rely on a small number of fundamental parameters to simulate observables over many orders of magnitude.
I will show how generative neural networks can be used to supplement these simulations in order to match precision requirements of future collider experiments. In addition one can use flow based invertible networks to invert the simulations chain and unfold detector level events to understand the mechanisms at the heart of proton collisions.

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