Integration of Combine with FCCAnalyses

Description

Combine (also known as CMS Combine) is a domain-agnostic statistical tool designed for High Energy Physics that facilitates model building, statistical testing, and result validation. Originally developed to search for the Higgs boson, it has since become the standard tool for a wide range of measurements. It enables physicists to compare expected observation models against experimental data to perform tasks such as claiming particle discoveries, setting new physics limits, and measuring cross sections. The package is versatile enough to handle a broad range of HEP workflows, including simple counting experiments, unfolded measurements, and complex Effective Field Theory (EFT) fits.

FCCAnalyses is a high-performance Python/C++ framework designed for physics reach studies and detector optimizations of the Future Circular Collider, implemented within the Key4hep ecosystem. Built upon ROOT’s RDataFrame, it enables efficient, multi-threaded processing of EDM4hep datasets, allowing physicists to define complex event selections and calculate observables with minimal overhead. The framework acts as a bridge between reconstructed data and final statistical inputs, providing a standardized set of analysis building blocks, ranging from the calculation of simple physics variables to jet clustering or flavor tagging.

This project aims to streamline the transition from event selection to statistical inference within the Future Circular Collider (FCC) analysis software ecosystem. Currently, FCCAnalyses excels at processing EDM4hep events into histograms; however, performing statistical fits often requires manual, error-prone translation into external tools. This project will develop a native interface to Combine, allowing users to interact with the tool through an integrated workflow. By automating the underlying generation of datacards and workspaces, this interface will enable seamless limit setting, significance tests, and precision measurements.

Expected Results and Milestones

Requirements

AI Policy

AI assistance is allowed for this contribution. The applicant takes full responsibility for all code and results, disclosing AI use for non-routine tasks (algorithm design, architecture, complex problem-solving). Routine tasks (grammar, formatting, style) do not require disclosure.

How to Apply

Write an email to the mentors with a brief introduction of your interests and background. Please include “gsoc26” in the subject line. The mentors will provide you with a small evaluation task afterwards.

Resources

Mentors

Additional Information

Corresponding Project

Participating Organizations