The focus of CHEP evolves over time to reflect advances in technology and the changing needs of scientific research. In 2026, CHEP will take place during the final week of Run 3 operations at the LHC. As the community prepares for the High-Luminosity LHC (HL-LHC), the conference will cover a wide range of emerging topics in both infrastructure and software. Reflecting broader trends, CHEP 2026 will also include sessions on sustainable computing, such as green data centers, energy consumption, and the role of AI/ML in operations.
Although the name CHEP refers to High Energy and Nuclear Physics, the conference welcomes contributions from a wide range of data-intensive scientific disciplines. It provides an excellent opportunity for interdisciplinary exchange, including -but not limited to- topics in big data applications for astronomy, biology, medicine, and quantum computing. Experiences and contributions from High Performance Computing (HPC) centers are also highly welcomed, especially where they intersect with challenges in large-scale data processing, simulation, and infrastructure design.
Track 1 - Data and metadata organization, management and access
Storage management frameworks; data access protocols; object, metadata and event store systems; content delivery and caching; data analytics; FAIR data principles; non-event data; data classification (including ML); online and offline databases; exabyte-scale datasets.
Track 2 - Online and real-time computing
Data acquisition; triggers; streaming and trigger-less data acquisition; online calibration/reconstruction; real-time analysis; event building; configuration and access controls; detector control systems; real-time analytics and monitoring; trigger techniques and algorithms; hardware trigger algorithms; ML for triggers or outlier detection; accelerators and hybrid computing for online computing.
Track 3 - Offline data processing
Offline reconstruction; object identification; object calibration; detector calibration; data quality systems; data preparation; physics performance; compute accelerators and hybrid computing for offline; ML for offline computing/calibration/outlier detection; quantum algorithms and general quantum computing technologies.
Track 4 - Distributed computing
Grid middleware; monitoring and accounting frameworks; security models and tools; distributed workload management; federated authentication and authorisation infrastructures; middleware databases; software distribution and containers; heterogeneous resource brokerage.
Track 5 - Event generation and simulation
MC event generation; theory calculations; detector geometries; detector simulation; fast simulation (classic and ML); quantum simulation and algorithms; accelerators and hybrid computing for generation/simulation software.
Track 6 - Software environment and maintainability
Software development; sustainable software; software management, continuous integration; software building; testing and quality assurance; software distribution; programming techniques and tools; integration of external toolkits; Manuals and documentation; ML for documentation, LLMs.
Track 7 - Computing infrastructure and sustainability
Opportunistic resources, orchestration of virtual machines and containers; cloud; HPC and exascale; networking; computing centre infrastructure; energy efficiency; environmental impact, and sustainable computing practices; cost of computing; management and monitoring; quantum networks.
Track 8 - Analysis infrastructure, outreach and education
Infrastructure for interactive computing; applications and use-cases; experience with analysis facility production systems and pilots; aspects of reproducibility in interactive computing; collaboration enabling tools; reinterpretation tools; analysis preservation and reuse; data preservation for collaboration; outreach activities; open data for education and training; training initiatives; event displays.
Track 9 - Analysis software and workflows
Software for end-user analysis; analysis frameworks; ML in analysis workflows; analysis workflows;
