GPT-like transformer model for silicon tracking detector simulation

TL;DR

This paper introduces a GPT-like transformer neural network for simulating silicon tracking detectors in high energy physics, achieving comparable performance to full simulations while capturing correlations between hits.

Contribution

It is the first to apply a transformer-based neural network for silicon tracker simulation, demonstrating its effectiveness in this domain.

Findings

Transformer architecture is optimal for silicon detector simulation.

The model achieves tracking performance comparable to full simulation.

Full hit correlations are preserved in the generative process.

Abstract

Simulating physics processes and detector responses is essential in high energy physics and represents significant computing costs. Generative machine learning has been demonstrated to be potentially powerful in accelerating simulations, outperforming traditional fast simulation methods. The efforts have focused primarily on calorimeters. This work presents the very first studies on using neural networks for silicon tracking detectors simulation. The GPT-like transformer architecture is determined to be optimal for this task and applied in a fully generative way, ensuring full correlations between individual hits. Taking parallels from text generation, hits are represented as a flat sequence of feature values. The resulting tracking performance, evaluated on the Open Data Detector, is comparable with the full simulation.