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Event Generation with Normalizing Flows
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Event Generation with Normalizing Flows
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We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.
Forward citations
Cited by 9 Pith papers
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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-globa...
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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
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Resonance-aware parton-shower matching for off-shell top-antitop production with semi-leptonic decays at electron-positron colliders
A resonance-aware MC@NLO matching procedure preserves top-antitop line shapes when NLO QCD predictions for off-shell ttbar production at e+e− colliders are showered with Pythia8.
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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 coupl...
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A universal vision transformer for fast calorimeter simulations
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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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