Title: Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters,
Abstract: Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of
particle collisions to build expectations of what experimental data may look
like under different theory modeling assumptions. Petabytes of simulated data
are needed to develop analysis techniques, though they are expensive to
generate using existing algorithms and computing resources. The modeling of
detectors and the precise description of particle cascades as they interact
with the material in the calorimeter are the most computationally demanding
steps in the simulation pipeline. We therefore introduce a deep neural
network-based generative model to enable high-fidelity, fast, electromagnetic
calorimeter simulation. There are still challenges for achieving precision
across the entire phase space, but our current solution can reproduce a variety
of particle shower properties while achieving speed-up factors of up to
100,000$\times$. This opens the door to a new era of fast simulation that could
save significant computing time and disk space, while extending the reach of
physics searches and precision measurements at the LHC and beyond.