The RooStats Project

TL;DR

RooStats is a C++ toolkit built on RooFit that provides advanced statistical methods for analyzing LHC data, focusing on discovery, confidence intervals, and combined measurements in complex models.

Contribution

It introduces a coherent set of statistical classes for LHC data analysis, supporting various techniques like likelihood-based, frequentist, and Bayesian methods.

Findings

Provides a unified framework for statistical analysis in high-energy physics.

Supports complex models with multiple parameters and nuisance parameters.

Enables digital publication of analysis results.

Abstract

RooStats is a project to create advanced statistical tools required for the analysis of LHC data, with emphasis on discoveries, confidence intervals, and combined measurements. The idea is to provide the major statistical techniques as a set of C++ classes with coherent interfaces, so that can be used on arbitrary model and datasets in a common way. The classes are built on top of the RooFit package, which provides functionality for easily creating probability models, for analysis combinations and for digital publications of the results. We will present in detail the design and the implementation of the different statistical methods of RooStats. We will describe the various classes for interval estimation and for hypothesis test depending on different statistical techniques such as those based on the likelihood function, or on frequentists or bayesian statistics. These methods can be applied in complex problems, including cases with multiple parameters of interest and various nuisance parameters.