Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research

TL;DR

This paper introduces REDVID, a simplified, parametric simulation tool for high-energy physics that facilitates efficient data generation and ML model development, addressing computational challenges in particle tracking.

Contribution

The paper presents REDVID, a flexible, open-source, complexity-reduced detector model and simulator that accelerates ML research in high-energy physics by simplifying data generation.

Findings

REDVID enables efficient synthetic data generation for ML research.

It significantly reduces computational costs compared to traditional simulations.

The tool is publicly available and supports rapid ML model development.

Abstract

Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo. REDVID is intended as a simulation-in-the-loop, to both generate synthetic data efficiently and to simplify the challenge of ML model design. The fully parametric nature of our tool, with regards to system-level configuration, while in contrast to physics-accurate simulations, allows for the generation of simplified data for research and education, at different levels. Resulting from the reduced complexity, we showcase the computational efficiency of REDVID by providing the computational cost figures for a multitude of simulation benchmarks. As a simulation and a generative tool for ML-assisted solution design, REDVID is highly flexible, reusable and open-source. Reference data sets generated with REDVID are publicly available. Data generated using REDVID has enabled rapid development of multiple novel ML model designs, which is currently ongoing.