Speaker
Susie Kim
(ITP, Heidelberg University)
Description
Due to the non-perturbative nature of hadronization, its simulation relies on a fragmentation function of a fixed parametric form. We present HOMER, a data-driven alternative based on neural networks that extracts the Lund string fragmentation function directly from data. HOMER addresses the information gap between the latent and observable phase spaces through an iterative reweighting procedure. We explore how far this approach can be pushed by increasing the complexity of the string configurations, which widens the information gap, and by departing from the assumption of the reference simulation.
Authors
Ayodele Ore
MLHAD Collaboration
Manuel Szewc
Dr
Sofia Palacios Schweitzer
(ITP, Heidelberg University)
Susie Kim
(ITP, Heidelberg University)
Tilman Plehn