HS3: A Descriptive, Interoperable Serialization Standard for Statistical Models in High-Energy Physics

Abstract

Statistical models in high-energy physics formally encode the relationship between observed data, physics parameters of interest, and experimental and theoretical uncertainties. Likelihood-based inference is the central tool for precision measurements, effective field theory fits, and cross-analysis combinations. Consequently, there is an increasing need for machine-readable, descriptive, and portable model representations. Existing formats such as ROOT workspaces, pyhf JSON, and CMS DataCards provide valuable capabilities but remain tied to specific software stacks and offer no universal standard for exchange, validation, or long-term preservation. We introduce HS3, the High-Energy Physics Statistics Serialization Standard, an implementation-agnostic, human-readable, and extensible serialization format for statistical models. HS3 is designed such that new statistical constructs can be incorporated through backward-compatible extensions, while inference procedures and implementation-specific execution details remain the responsibility of downstream frameworks. HS3 represents likelihoods as computational graphs composed of named distributions, functions, datasets, domains, and analysis prescriptions. It supports binned and unbinned likelihoods as well as hierarchical composite models. HS3 is convertible from and to ROOT/RooFit and is a superset of pyhf. We describe the design principles, structure, and semantics of HS3 and summarize existing implementations in C++, Python, and Julia. We also present early applications to public likelihoods on HEPData, cross-framework validation, and reproducibility efforts. HS3 provides a foundation for FAIR (Findable, Accessible, Interoperable, Reusable), long-lived statistical models at the LHC and beyond. The standard is intended to serve the broader scientific community and to evolve over time for application across a wide range of domains.