Neural refinement of Monte Carlo sample weights via phase-space scaling and a new resampling protocol that maintains averages and uncertainties.
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MAcNLOPS is implemented for ZZ production at the LHC, eliminating negative H weights via a shower veto on S events while agreeing with MC@NLO except for small low-pT effects.
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
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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.
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MAcNLOPS for ZZ Pair Production at the LHC
MAcNLOPS is implemented for ZZ production at the LHC, eliminating negative H weights via a shower veto on S events while agreeing with MC@NLO except for small low-pT effects.
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.