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.
Bias correction of gcm precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?Journal of Climate, 28(17):6938–6959
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.