Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters

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 theoretical 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 speedup factors of up to $100000\times$. 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.

Figure 1

Average $\gamma$ geant4 shower (top row), and average $\gamma$ calogan shower (bottom row), with progressive calorimeter depth (from left to right).

Figure 2

Five randomly selected $\gamma$ showers per calorimeter layer from geant4 (top rows) and their five nearest neighbors (by Euclidean distance) from a set of calogan candidates.

Figure 3

Comparison of shower shape variables and other variables of interest, such as the sparsity level per layer, for the geant4 and calogan data sets for $e^{+}$, $\gamma$, and ${\pi }^{+}$. See Ref. [33] for detailed definition.