Modeling hadronization using machine learning
We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions,...
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| Format: | Article |
| Language: | English |
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SciPost
2023-03-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.14.3.027 |