Iterative HOMER with uncertainties

Anja Butter, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn, Benoit Assi, Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan

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