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 | American 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 - 1 Feb 2023 |
Keywords
- Artificial Intelligence
- Bayesian optimization
- ECCE
- Electron Ion Collider
- Evolutionary algorithms
- Tracking
All Science Journal Classification (ASJC) codes
- Nuclear and High Energy Physics
- Instrumentation
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In: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1047, 167748, 01.02.2023.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider
AU - Fanelli, C.
AU - Papandreou, Z.
AU - Suresh, K.
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AU - Schmidt, A.
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AU - Xiao, Z.
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AU - Yang, Y.
AU - Ye, Z.
AU - Yoo, H. D.
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AU - Zachariou, N.
AU - Zajc, W. A.
AU - Zha, W.
AU - Zhang, J. L.
AU - Zhang, J. X.
AU - Zhang, Y.
AU - Zhao, Y. X.
AU - Zheng, X.
AU - Zhuang, P.
N1 - Publisher Copyright: © 2022 Elsevier B.V.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Bayesian optimization
KW - ECCE
KW - Electron Ion Collider
KW - Evolutionary algorithms
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=85145770275&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.nima.2022.167748
DO - https://doi.org/10.1016/j.nima.2022.167748
M3 - Article
SN - 0168-9002
VL - 1047
JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
M1 - 167748
ER -