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The MadNIS Reloaded
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In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.
Forward citations
Cited by 10 Pith papers
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Neural Control Variates at LO and NLO
Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.
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Automated NRQCD and NRQED simulations of quarkonium and leptonium production with P-wave states and physical-mass effects
MadSONS extends MadGraph to automated LO NRQCD/NRQED event generation for arbitrary S- and P-wave bound states, with dual-number projectors and physical-mass reshuffling.
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Agentic Re-Casting using Agentic Re-Simulations
An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.
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An Optimal Transportation Approach for Improved Confidence Intervals
Optimal-transport couplings are used to construct confidence intervals that reduce coverage error relative to classical quantile-based intervals, with consistency theory and data-driven hyperparameters.
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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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An Optimal Transportation Approach for Improved Confidence Intervals
An optimal transport method is proposed to construct confidence intervals with improved coverage, including theoretical consistency results, error bounds, and simulation comparisons.
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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.
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Monte Carlo Event Generators for Future Lepton Colliders
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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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer by six leading developers maps the full Monte Carlo chain (matrix elements, parton showers, hadronisation, detector simulation, tuning, analysis) and the computing and reproducibility issues that come with it.
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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer that surveys the architecture, methodologies, computational challenges, and future trajectory of the Monte Carlo event generator ecosystem in collider physics.
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