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

arXiv: https://arxiv.org/abs/2205.09185 · HTML: https://mlsanity.com/paper/2205.09185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 4 of 4)

- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case](https://mlsanity.com/paper/1911.05797/) (2019)
- [Design of Detectors at the Electron Ion Collider with Artificial Intelligence](https://mlsanity.com/paper/2203.04530/) (2022)

## Built on by (most cited first, 5 of 5)

- [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://mlsanity.com/paper/2307.08593/) (2023)
- [AI-Assisted Detector Design for the EIC (AID(2)E)](https://mlsanity.com/paper/2405.16279/) (2024)
- [The Optimal use of Segmentation for Sampling Calorimeters](https://mlsanity.com/paper/2310.04442/) (2023)
- [Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing](https://mlsanity.com/paper/2603.30014/) (2026)
- [Meta-learning accelerates detector design optimization](https://mlsanity.com/paper/2609.35827/) (2026)

## Abstract (arXiv)

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.