Hadronization is an important step in Monte Carlo event generators, where quarks and gluons are bound into physically observable hadrons. Today’s generators rely on finely-tuned empirical models, such as the Lund string model; while these models have been quite successful overall, there remain phenomenological areas where they do not match data well. In this talk, we present MLHad, a machine-learning-based alternative for generating hadronization chains, which we intend ultimately to be data-trainable. Latent-space vectors are encoded, trained to be distributed according to a user-defined distribution using the sliced-Wasserstein distance in the loss function, then decoded to simulate hadronization.
We show that generated pion multiplicities and cumulative kinematic distributions match those generated using Pythia (arXiv:2203.04983). We also present our more-recent work using normalizing flows to generate non-pion hadrons and to propagate errors through the encoder and decoder. Finally, we present comparisons with empirical data.
|Consider for long presentation||Yes|