AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

TL;DR

This paper presents an AI-driven optimization approach for the ECCE tracking system at the Electron-Ion Collider, enhancing detector design by navigating complex multidimensional parameter spaces with multiple objectives.

Contribution

It introduces a comprehensive AI-based optimization method for the ECCE detector tracker, applicable across various sub-detectors and adaptable to different simulation frameworks.

Findings

Optimized ECCE tracker design with improved performance metrics

Demonstrated AI approach's flexibility and simulation framework agnosticism

Achieved multi-objective optimization satisfying mechanical constraints

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 1.5T 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.