Russia Has Learned to Rapidly Match AI Models for Particle Accelerators

Fewer Computations, Higher Reliability

Researchers at the HSE University Center for Artificial Intelligence have proposed a method that significantly accelerates the selection of machine‑learning models for particle‑accelerator experiments. Traditionally, building and training neural networks requires numerous attempts and extensive manual validation. The new algorithm reduces the volume of computations by roughly a factor of eight.

A Stress Test for Repeatability

The algorithm automatically compares dozens of neural‑network configurations, evaluating the stability of each model’s outputs. Every model is trained multiple times—with different initial parameters and slightly altered input data. The system analyzes how much the error metrics vary between training runs and selects only those models that consistently produce stable results. This is especially important in experimental physics, where accuracy and repeatability are critical for interpreting scientific data.

Designed for Colliders

For testing, the researchers used a dataset collected from one of the accelerator’s sensors, which records both energy and particle‑direction measurements. The results showed that the optimal model can be identified after approximately 41,500 training attempts—whereas a standard exhaustive search would require eight times more. This dramatically accelerates algorithm development for large scientific facilities, including particle colliders.

Beyond Physics

According to the developers, this method has applications far beyond particle physics—in materials science, medical diagnostics, and the analysis of complex signals. It speeds up model preparation, reduces the workload on researchers, and allows them to focus on interpreting results rather than managing computational pipelines.