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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A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.
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 framework based on the YFS theorem enables process-independent local IR subtraction and resummation matching for automated NNLO_EW calculations in lepton collider processes.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
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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Data-Driven Predictions for Dark Photon and Millicharged Particle Production
A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.
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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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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.
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Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.