Speaker
Ayodele Ore
Description
Significant efforts are currently underway to improve the description of hadronization using Machine Learning. While modern generative architectures can undoubtedly emulate observations, it remains a key challenge to integrate these networks within principled fragmentation models in a consistent manner. This talk presents developments in the HOMER method for extracting Lund fragmentation functions from experimental data. We tackle the information gap between latent and observable phase spaces, and quantify uncertainties with Bayesian neural networks.
Authors
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Ayodele Ore
Manuel Szewc
Sofia Palacios Schweitzer
(ITP, University Heidelberg)
Tilman Plehn