The 23rd International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2025) took place between Monday 8th and Friday, 12th September, 2025 at the University of Hamburg downtown campus. ACAT 2025 was jointly organised by DESY and the University of Hamburg.

The 23rd edition of ACAT did — once again — bring together computational experts from a wide range of disciplines, including particle-, nuclear-, astro-, and accelerator-physics as well as high performance computing. Through this unique forum, we explore the areas where these disciplines overlap with computer science, fostering the exchange of ideas related to cutting-edge computing, data-analysis, and theoretical-calculation technologies.

Transforming the Scientific Process: AI at the Heart of Theory, Experiment, and Computation in High-Energy and Nuclear Physics

The scientific process in high-energy and nuclear physics is undergoing a profound transformation, driven by the integration of artificial intelligence across all facets of research. On the theoretical front, AI is unlocking new ways to bridge the gap between experimental data and fundamental insights. By tackling complex inverse problems and enhancing predictive models, AI tools are empowering physicists to better map experimental results to theoretical parameters and accelerate joint experimental-theoretical analysis, leading to a deeper understanding of the universe's most fundamental forces.

In experiments, AI is pushing the boundaries of precision and sensitivity. Whether it's improving data reconstruction, refining object identification and classification, or advancing final calibrations, AI is revolutionizing how experiments are conducted and analyzed. The incorporation of AI-driven uncertainty quantification ensures more reliable results, while innovative workflows streamline processes, enabling faster and more accurate discoveries.

This transformation is underpinned by advancements in computational methods, where resource-aware AI models are rising to the challenge of operating in constrained environments like ASICs and FPGAs. AI-powered autonomous systems are enabling smarter control of experimental setups, from detectors to accelerators. Digital twins and robust co-design strategies are fostering trust in AI-based decision-making, paving the way for seamless integration of computational and experimental systems.

Together, these developments tell a story of a field redefined by AI—a cohesive interplay of theory, experimentation, and computation working in harmony to transform the scientific process for a new era of discovery.

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