A parton-shower-inspired local subtraction scheme for double-real corrections in color singlet decays is introduced, with finiteness verified for the e+e- to qqbar remainder and phase-space integrals computed analytically and via sector decomposition.
Bothmannet al.(Sherpa), Event generation with Sherpa 3, JHEP12, 156, arXiv:2410.22148 [hep-ph]
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Charged energy correlators measured in jets at the LHC show like-sign pairs sensitive to parton shower models and unlike-sign pairs sensitive to hadronization, with charge-independent cold nuclear matter effects in p-Pb collisions.
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.
A reweighting method is introduced to efficiently study and tune MPI and CR parameter variations in Sherpa using relative weights, demonstrated with LHC and Tevatron data.
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.
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.
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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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.