Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations

• URL: http://arxiv.org/abs/2605.07460v1
• Date: Fri, 08 May 2026 09:07:18 GMT
• Title: Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
• Abstract summary: Monte Carlo modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data.<n>In practice, experimental information is often limited to one-dimensional (1D) distributions, while mismodelling arises in a multidimensional feature space.<n>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.
• Score: 10.078675569996992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 original simulation. This minimal-deviation principle preserves the global correlation structure of the baseline model while enabling targeted corrections of mismodelled features. Using controlled studies with simulated pseudo-data, we show that the method improves agreement with target distributions and maintains a consistent multidimensional structure. The approach is designed for complex, high-dimensional analyses where traditional techniques are insufficient, providing a scalable way to enhance MC modelling under limited information.

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