GPT-like transformer model for silicon tracking detector simulation

by Tadej Novak (Jožef Stefan Institute)

Thursday 29 Jan 2026, 10:30 → 11:30 Europe/Ljubljana

C/0-C084 - F9 seminarska soba (Jamova)

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 seminar 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 tracking performance, evaluated on the Open Data Detector, is presented for single muons, electrons and pions. Benchmarking is performed on recent generations of GPUs to quantify the computing costs of such simulation setup.