Advanced Computing and Analysis Techniques in Physics Research

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.

Fundamental research is dealing, by definition, with the two extremes: the extremely small and the extremely large. The LHC and Astroparticle physics experiments will soon offer new glimpses beyond the current frontiers. And the computing infrastructure to support such physics research needs to look beyond the cutting edge.

Once more it seems that we are on the edge of a computing revolution. But perhaps what we are seeing now is a even more epochal change where not only the pace of the revolution is changing, but also its very nature. Change is not any more an "event" meant to open new possibilities that have to be understood first and exploited then to prepare the ground for a new leap. Change is becoming the very essence of the computing reality, sustained by a continuous flow of technical and paradigmatic innovation.

The hardware is definitely moving toward more massive parallelism, in a breathtaking synthesis of all the past techniques of concurrent computation. New many-core machines offer opportunities for all sorts of Single/Multiple Instructions, Single/Multiple Data and Vector computations that in the past required specialised hardware.

At the same time, all levels of virtualisation imagined till now seem to be possible via Clouds, and possibly many more. Information Technology has been the working backbone of the Global Village, and now, in more than one sense, it is becoming itself the Global Village. Between these two, the gap between the need for adapting applications to exploit the new hardware possibilities and the push toward virtualisation of resources is widening, creating more challenges as technical and intellectual progress continues. ACAT 2010 proposes to explore and confront the different boundaries of the evolution of computing, and its possible consequences on our scientific activity.

What do these new technologies entail for physics research? How will physics research benefit from this revolution in data taking and analysis, experiment monitoring and complex simulations? What physics research seizing these new technologies may bring forward innovations that would benefit the society at large?

Editorial board: T. Speer (chairman), F. Boudjema, J. Lauret, A. Naumann, L. Teodorescu, P. Uwer

Conference web-site: http://acat2010.cern.ch/
Programme and presentations: http://indico.cern.ch/conferenceDisplay.py?confId=59397

Editorial board Thomas Speer (chairman), Federico Carminati and Monique Werlen

1. Computing Technology for Physics Research
• Multilevel parallelism
• Distributed computing
• New architectures
• High precision computing
• Software quality control
• User Interfaces, Common Libraries
• Online Monitoring and Control
2. Data Analysis - Algorithms and Tools
• Neural Networks and Other Pattern Recognition Techniques
• Evolutionary and Genetic Algorithms
• Advanced Data Analysis Environments
• Statistical Methods
• Detector and Accelerator Simulations
• Reconstruction Algorithms
• Visualization Techniques
3. Methodology of Computations in Theoretical Physics
• Automatic Computation Systems: from Processes to Event Generators
• Multi-dimensional Integration and Event Generators
• Intensive High Precision Numerical Computations: Algorithms and Systems
• Computer Algebra Techniques and Applications