  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
1.5T 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.
