Abstract

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the ‘‘glue’’ that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5 T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

Document Type

Article

Publication Date

2023

Notes/Citation Information

0168-9002/© 2022 Elsevier B.V. All rights reserved.

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.nima.2022.167748

Funding Information

We acknowledge support from the Office of Nuclear Physics in the Office of Science in the Department of Energy, USA, the Na- tional Science Foundation, USA, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD), USA 20200022DR.

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