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May 8 – 12, 2023
Norfolk Waterside Marriott
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MLHad: Simulating Hadronization with Machine Learning

May 8, 2023, 11:00 AM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Wilkinson, Michael (University of Cincinnati)


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

Primary authors

Ilten, Philip (Massachusetts Institute of Technology) Menzo, Tony Mrenna, Steve Szewc, Manuel Wilkinson, Michael (University of Cincinnati) Youssef, Ahmed Zupan, Jure (U. Cincinnati)

Presentation materials