Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
van Beekveldet al., Phys
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PanScales final-state showers now include quark masses at NLL accuracy while keeping original accuracy for massless observables.
Implementing improved logarithmic accuracy parton showers in Herwig reveals that differences in infrared cutoffs have important effects on hadron-level predictions and tunability.
citing papers explorer
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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
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Logarithmically-accurate showers with massive quarks
PanScales final-state showers now include quark masses at NLL accuracy while keeping original accuracy for massless observables.
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Studying the Infrared Behaviour of Improved Logarithmic Accuracy Parton Showers with Herwig
Implementing improved logarithmic accuracy parton showers in Herwig reveals that differences in infrared cutoffs have important effects on hadron-level predictions and tunability.
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