The diagram emphasizes the close connections between theory, computations (both computational science and data science as well as many elements from computer science) and experiments. Taken from Machine Learning in Nuclear Physics, Reviews of modern physics 94.3 (2022): 031003..
ROLE OF AI/ML in THE EIC SCIENCE
Artificial Intelligence will contribute to all phases of the EIC starting from the Design and R&D. The optimization of such large scale experiment is a complex problem characterized by multiple parameters and objectives like detector performance and costs. AI will provide insight on hidden correlations among the design parameters and will identify optimal tradeoff solutions in a multidimensional space of the objectives. The AI-supported Optimization of the Accelerator and Detector Design needs reliable Simulations followed by Reconstruction and Analysis, all areas covered in the AI4EIC workshop.
AI/ML are already playing a significant role in optimizing the design of EIC experiments. For instance, in NIM-A: 1047 (2023): 167748, complex multi-objective optimization pipelines are employed to design the inner tracker for the central detector proposed in 2023—an effort that includes an interactive visualization of the Pareto front. Similarly, JINST 19 P06002 demonstrates the use of deep learning techniques for reconstructing signals in a sampling calorimeter, contributing to overall detector design and optimization.
Generative AI methods are actively being explored within the EIC community to accelerate simulation and reconstruction workflows. These approaches show particular promise in modeling complex response patterns in sub-detector systems—such as Cherenkov detectors—where they can enable fast and accurate charged-particle identification. Preliminary results suggest potential speedups of several orders of magnitude compared to traditional methods (see, e.g., MLST 6.1 (2025): 015028; arXiv:2504.19042(2025)).
Another important activity in EIC is Streaming Readout, aiming at a continuous readout of all detector signals with data selection realized in software, furthering the convergence of online and offline analyses and allowing for faster data quality monitoring, calibration and alignments. EIC stands poised to become one of the first large-scale experiments to leverage AI for autonomous experimentation and near real-time control. EIC operations are planned for the 2030s.
FURTHER INFORMATION
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AI4EIC paper
This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments
https://doi.org/10.1007/s41781-024-00113-4