CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

TL;DR

CaloGAN is a novel GAN-based method that significantly accelerates the simulation of electromagnetic particle showers in calorimeters, offering speedups of up to 100,000 times on GPUs while maintaining key physical properties.

Contribution

The paper introduces CaloGAN, a GAN-based approach for fast simulation of particle showers, demonstrating substantial speed improvements over traditional methods.

Findings

Achieves up to 10^5× speedup on GPU

Reproduces geometric shower shape properties

Potential for full neural network-based detector simulation

Abstract

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce \textsc{CaloGAN}, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter, and achieve speedup factors comparable to or better than existing full simulation techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to $\sim10^5\times$). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.