RooUnfold - Efficient deconvolution using state of the art algorithms


RooUnfold is a powerful modular analysis tool used for the unfolding (AKA “deconvolution” or “unsmearing”) of distributions. Unfolding is the statistical process in which the ‘true’ underlying processes that govern the interactions of fundamental forces of the universe are estimated from both simulated and measured data. To facilitate these data intensive analyses, RooUnfold is built on the ROOT Software Framework and is used in the publication of hundreds of LHC analyses every year. RooUnfold was designed to provide a common interface for a number of analysis algorithms and was recently upgraded to leverage the power of the ROOT statistical modelling package RooFit in order to provide a statistical comparison of the various algorithms.

This project aims to address obsolescence in the underlying RooUnfold implementation that would allow the RooUnfold package to act as a lightweight member class to ROOT whilst retaining its functionality and the ability for users to define new algorithms including machine learning based solutions, and data intensive tasks.

Task Ideas

Expected results

Working implementation of a new C++ base class that is optimised for usage with abstracted unfolding algorithms.


C++, CMake, python


Corresponding Project

Participating Organizations