# AI-Assisted Detector Design for the EIC (AID(2)E)

arXiv: https://arxiv.org/abs/2405.16279 · HTML: https://mlsanity.com/paper/2405.16279/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 7 of 7)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case](https://mlsanity.com/paper/1911.05797/) (2019)
- [AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider](https://mlsanity.com/paper/2205.09185/) (2022)
- [Towards a RAG-based Summarization Agent for the Electron-Ion Collider](https://mlsanity.com/paper/2403.15729/) (2024)
- [JMS: A workflow management system and web-based cluster front-end for the Torque resource manager](https://mlsanity.com/paper/1501.06907/) (2015)

## Built on by (most cited first, 3 of 3)

- [Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2603.28804/) (2026)
- [Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing](https://mlsanity.com/paper/2603.30014/) (2026)
- [RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering](https://mlsanity.com/paper/2604.22755/) (2026)

## Abstract (arXiv)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using Geant4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.