HistFitter software framework for statistical data analysis

TL;DR

HistFitter is a flexible, object-oriented software framework used by the ATLAS Collaboration for statistical analysis of large datasets from the LHC, enabling complex data modeling, fitting, and result presentation.

Contribution

It introduces a novel, core-analysis-inspired design that simplifies building, fitting, and interpreting complex data models with multiple hypotheses in particle physics.

Findings

Widely used in ATLAS supersymmetry searches since 2012

Supports multiple data models simultaneously with high abstraction

Provides publication-quality result presentation tools

Abstract

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fitted to data and interpreted with statistical tests. A key innovation of HistFitter is its design, which is rooted in core analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its very fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple data models at once, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication-quality style through a simple command-line interface.