Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experimental information is often limited to one-dimensional (1D) distributions, while mismodelling arises in a multidimensional feature space. This restricts traditional correction methods, as one-dimensional reweighting ignores correlations and fully multidimensional approaches require large target datasets. We propose a neural network-based method that operates under these constraints by learning a transformation of simulated events that reproduces the available 1D target distributions while remaining close to the o...
| Authors | Matthias Schott, Lucie Flek |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 21 August 2026 |
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