Neural refinement of Monte Carlo sample weights via phase-space scaling and a new resampling protocol that maintains averages and uncertainties.
Ninja: Automated Integrand Reduction via Laurent Expansion for One-Loop Amplitudes
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abstract
We present the public C++ library Ninja, which implements the Integrand Reduction via Laurent Expansion method for the computation of one-loop integrals. The algorithm is suited for applications to complex one-loop processes.
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MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.
Higher-order QCD predictions for pp to tW enable three-parameter SMEFT fits that constrain effective new-physics scales to 0.5–2 TeV using LHC Run II and III data.
Reviews selected challenges in Monte Carlo event generators for future lepton colliders including electroweak corrections, initial-state radiation, beam dynamics, perturbative QCD and non-perturbative modelling.
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Stay Positive: Neural Refinement of Sample Weights
Neural refinement of Monte Carlo sample weights via phase-space scaling and a new resampling protocol that maintains averages and uncertainties.