Correcting measured detector-level distributions to particle-level is essential to make data usable outside the experimental collaborations. The term unfolding is used to describe this procedure. A new method of unfolding data using a modified Generative Adversarial Network (MSGAN) is presented here. Applied to various distributions with widely different shapes, it performs roughly at par with currently used methods. This is a proof-of-principle demonstration of a state-of-the-art machine learning method that can be used to model detector effects well.
@article{arxiv.1806.00433,
title = {Unfolding with Generative Adversarial Networks},
author = {Kaustuv Datta and Deepak Kar and Debarati Roy},
journal= {arXiv preprint arXiv:1806.00433},
year = {2018}
}
Comments
11 pages, 10 figures, prepared for submission to JHEP