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

By Michela Paganini, Luke de Oliveira, Benjamin Nachman. 2017

252 citations recorded by the source corpus.

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 100 000×. 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.

Subfields: Nuclear and High Energy Physics

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In Nuclear and High Energy Physics

1. Konstantin Shmelkov
2. Cordelia Schmid
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Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, background): “s. For even longerrun risk evaluation, it is possible to simulate crop yield via biological and ecological models [583–585], presenting another opportunity for blending large scale simulation with ML [586, 587]. Beyond sounding alarms, ML can improve resilience of food supply chains. As detailed in ”
• How good is my GAN? (2018, methodology): “ of synthetic images for applications such as eye gaze and hand pose estimation [49]. GANs are also used to learn complex 3D distributions and replace computationally intensive simulations in physics [39,42] and neuroscience [38]. Ideally, GANs should be able to recreate the training set with dieren”