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.
| Original language | English |
|---|---|
| Article number | 167748 |
| Journal | Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment |
| Volume | 1047 |
| DOIs | |
| State | Published - Feb 2023 |
Funding
We thank the EIC Silicon Consortium for cost estimate methodologies concerning silicon tracking systems, technical discussions, and comments. We acknowledge the important prior work of projects eRD16, eRD18, and eRD25 concerning research and development of MAPS silicon tracking technologies. We thank the EIC LGAD Consortium for technical discussions and acknowledge the prior work of project eRD112. We thank (list of individuals who are not coauthors) for their useful discussions, advice, and comments. We acknowledge support from the Office of Nuclear Physics in the Office of Science in the Department of Energy, USA, the National Science Foundation, USA, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD), USA20200022DR.
Keywords
- Artificial Intelligence
- Bayesian optimization
- ECCE
- Electron Ion Collider
- Evolutionary algorithms
- Tracking