Iterative HOMER with uncertainties
Abstract
We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.
Keywords
Cite
@article{arxiv.2509.03592,
title = {Iterative HOMER with uncertainties},
author = {Anja Butter and Ayodele Ore and Sofia Palacios Schweitzer and Tilman Plehn and Benoît Assi and Christian Bierlich and Philip Ilten and Tony Menzo and Stephen Mrenna and Manuel Szewc and Michael K. Wilkinson and Ahmed Youssef and Jure Zupan},
journal= {arXiv preprint arXiv:2509.03592},
year = {2026}
}
Comments
37 pages, 15 figures, 2 tables. v2: Minor revisions matching SciPost publication