Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking, in its current form, is exceptionally computationally challenging. Fielded solutions, relying on traditional algorithms, do not scale linearly and pose a major limitation for the HL-LHC era. Machine Learning (ML) assisted solutions are a promising answer. Current ML model design practice is predominantly ad hoc. We aim for a methodology for automated search of ML model designs, consisting of complexity reduced descriptions of the main problem, forming a complexity spectrum. As the main pillar of such a method, we provide the REDuced VIrtual Detector (REDVID) as a complexity-aware detector model and particle collision event simulator. Through a multitude of configurable dimensions, REDVID is capable of simulations throughout the complexity spectrum. REDVID can also act as a simulation-in-the-loop, to both generate synthetic data efficiently and to simplify the challenge of ML model design evaluation. Starting from the simplistic end of the spectrum, lesser designs can be eliminated in a systematic fashion, early on. REDVID is not bound by real detector geometries and can simulate arbitrary detector designs. As a simulation and a generative tool for ML-assisted solution design, REDVID is open-source and reference data sets are publicly available. It has enabled rapid development of novel ML models.
Odyurt et al. (2025) studied this question.