Answers are grounded in this paper and cite their sources.
Engineering report outlines artificial intelligence integration strategies for the European Spallation Source proton linac, highlighting improved accelerator commissioning and operational efficiency.
The European Spallation Source (ESS) is a multidisciplinary neutron research facility currently under commissioning in Lund, Sweden. It is driven by a superconducting proton linear accelerator (linac) that presents significant needs and opportunities for developing and implementing artificial intelligence (AI) and machine learning (ML) based activities. This work introduces the overall strategy for ESS linac to utilize different AI/ML techniques in the accelerator during both the commissioning and steady-state operation phases. We identify the use-cases that are most aligned with high-level goals of ESS and explore the possibilities of various AI-based tools for different parts of the accelerator while taking into consideration the successful experience in other facilities. Additionally, we discuss the ongoing preparatory activities to develop infrastructure facilitating the implementation of data-intensive projects. In the current phase of commissioning a number of pilot projects are already under development or being tested offline or at the accelerator. In addition to their primary goals, these projects are also meant to serve as catalyst for identifying the needs and gaps in the existing infrastructure and workflows.
No takes yet. Share an insight, caveat, or question.
Gorgisyan et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: