A neural network learns minimal-deviation transformations of simulated events to match 1D target distributions while preserving multi-dimensional correlation structure in HEP Monte Carlo models.
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Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
A neural network learns minimal-deviation transformations of simulated events to match 1D target distributions while preserving multi-dimensional correlation structure in HEP Monte Carlo models.