Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

Understand

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results.

• However, Monte Carlo based simulation is extremely demanding in terms of computing resources.
• This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo approach.
• We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude.