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.
Implementation of two NLL-accurate dipole showers in Herwig shows that differences in infrared cutoffs produce noticeable effects at the hadron level and affect model 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
Implementation of two NLL-accurate dipole showers in Herwig shows that differences in infrared cutoffs produce noticeable effects at the hadron level and affect model tunability.
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