# Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters

## TL;DR

This paper presents a deep neural network-based generative model that significantly accelerates the simulation of particle showers in calorimeters, achieving speed-ups of up to 100,000 times while maintaining high fidelity, thereby enabling more efficient physics research at the LHC.

## Contribution

The authors introduce a novel generative adversarial network for fast, high-fidelity simulation of electromagnetic calorimeter data, addressing computational challenges in particle physics simulations.

## Key findings

- Achieves up to 100,000× speed-up in simulation
- Reproduces key properties of particle showers accurately
- Potential to reduce computational costs significantly

## 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.