Design of Detectors at the Electron Ion Collider with Artificial Intelligence

TL;DR

This paper explores the use of artificial intelligence techniques for designing detectors at the Electron Ion Collider, aiming to optimize complex, multi-objective detector configurations efficiently.

Contribution

It introduces AI-based multi-objective optimization methods applied to EIC detector design, addressing challenges of high-dimensional, noisy, and non-differentiable simulation objectives.

Findings

AI can efficiently optimize complex detector designs.

Multi-objective optimization improves detector performance and cost-efficiency.

Progress made in applying AI techniques to EIC detector design.

Abstract

Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The Electron Ion Collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about $\pm$35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.