Speaker
Description
Monte Carlo simulations are an essential tool for data analysis in particle physics. Simulated events are typically produced alongside weights, that redistribute the production rate of a simulated process across the phase space. The presence of latent degrees of freedom can lead to a distribution of weights with negative values, often complicating analyses, especially if they involve machine learning methods. Traditional post-hoc reweighting methods aim to approximate the average weight as a function of phase space. In contrast, we propose a novel approach that refines the initial weights to eliminate negative values through a scaling transformation, utilizing a phase space dependent factor. Our method uses neural networks to process high-dimensional and unbinned phase spaces. We will show that our neural weight refinement method achieves comparable or superior accuracy to existing reweighting schemes, and demonstrate its behavior on realistic and synthetic examples.