Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two tasks and show how they both benefit from unbinned, high-dimensional unfolding. Unlike weight-based or iterative generative methods we include a targeted unbiasing with respect to the training data. This shows significant advantages over standard, iterative methods, in terms of applicability, flexibility and accuracy.
@article{arxiv.2501.12363,
title = {How to Unfold Top Decays},
author = {Luigi Favaro and Roman Kogler and Alexander Paasch and Sofia Palacios Schweitzer and Tilman Plehn and Dennis Schwarz},
journal= {arXiv preprint arXiv:2501.12363},
year = {2025}
}