# AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case

arXiv: https://arxiv.org/abs/1911.05797 · HTML: https://mlsanity.com/paper/1911.05797/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 15 of 15)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Meta-Learning Priors for Safe Bayesian Optimization](https://mlsanity.com/paper/2210.00762/) (2022)
- [Design of Detectors at the Electron Ion Collider with Artificial Intelligence](https://mlsanity.com/paper/2203.04530/) (2022)
- [AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider](https://mlsanity.com/paper/2205.09185/) (2022)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [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)
- [Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2603.28804/) (2026)
- [Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models](https://mlsanity.com/paper/2407.07376/) (2024)
- [Large Language Models for Physics Instrument Design](https://mlsanity.com/paper/2601.07580/) (2026)
- [Artificial Intelligence for Imaging Cherenkov Detectors at the EIC](https://mlsanity.com/paper/2204.08645/) (2022)
- [The Optimal use of Segmentation for Sampling Calorimeters](https://mlsanity.com/paper/2310.04442/) (2023)
- [Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization](https://mlsanity.com/paper/2510.26633/) (2025)
- [Bayesian Optimization on Networks](https://mlsanity.com/paper/2510.27643/) (2025)
- [Meta-learning accelerates detector design optimization](https://mlsanity.com/paper/2609.35827/) (2026)

## Abstract (arXiv)

Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. We propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as part of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our approach can be applied to any detector R&D, provided that realistic simulations are available.