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.
Bothmannet al.(Sherpa), Event generation with Sherpa 3, JHEP2024(12), 156, arXiv:2410.22148 [hep- ph]
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A resonance- and width-aware parton shower with NLO matching is developed for e+e- to W+W- bbbar, extending beyond standard Breit-Wigner approximations, with a public SHERPA-based simulator.
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
A method to approximate kinematic distributions from Monte Carlo events using coefficients of orthogonal basis functions produces smooth curves and removes bin-to-bin fluctuations in subtracted perturbative calculations.
Implementing improved logarithmic accuracy parton showers in Herwig reveals that differences in infrared cutoffs have important effects on hadron-level predictions and tunability.
A framework based on the YFS theorem enables process-independent local IR subtraction and resummation matching for automated NNLO_EW calculations in lepton collider processes.
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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Resonance- and Width-aware Parton Shower Evolution and NLO Matching
A resonance- and width-aware parton shower with NLO matching is developed for e+e- to W+W- bbbar, extending beyond standard Breit-Wigner approximations, with a public SHERPA-based simulator.
-
Monte Carlo Event Generation with Continuous Normalizing Flows
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
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On the reconstruction of kinematic distributions computed with Monte Carlo methods using orthogonal basis functions
A method to approximate kinematic distributions from Monte Carlo events using coefficients of orthogonal basis functions produces smooth curves and removes bin-to-bin fluctuations in subtracted perturbative calculations.
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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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Towards a Fully Automated Differential $\text{NNLO}_\text{EW}$ Generator for Lepton Colliders
A framework based on the YFS theorem enables process-independent local IR subtraction and resummation matching for automated NNLO_EW calculations in lepton collider processes.