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.
| Comments: | 12 pages, 6 figures |
| Subjects: | Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex) |
| Cite as: | arXiv:2605.07460 [cs.LG] |
| (or arXiv:2605.07460v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.07460 arXiv-issued DOI via DataCite (pending registration) |
From: Matthias Schott [view email]
[v1]
Fri, 8 May 2026 09:07:18 UTC (450 KB)
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