Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

TL;DR

This paper envisions an AI-native ecosystem for experimental particle physics, aiming to accelerate discovery through transformative AI integration across current and future large-scale facilities.

Contribution

It proposes a comprehensive community-driven vision for integrating AI into experimental particle physics, outlining challenges, opportunities, and collaborative strategies for future research infrastructure.

Findings

Current facilities can serve as proving grounds for AI integration.

AI can significantly enhance data analysis and discovery processes.

A collaborative national effort is essential for AI-native research ecosystems.

Abstract

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.