Speaker
Description
Accurate Monte Carlo (MC) modelling of high-energy physics (HEP) data remains a central challenge, especially when simulated distributions fail to reproduce observations. Traditional remedies rely on reweighting individual observables to data, an approach that is effective when only one or two dimensions exhibit discrepancies. However, for N correlated observables with N > 2, conventional reweighting becomes impractical due to the exponential growth of required statistics.
In this work, we investigate a transformer-based approach designed to learn corrections directly from data without relying on manual reweighting procedures. By training the network to reproduce multiple target distributions simultaneously, the method yields a generative model that captures complex correlations and mitigates mismodelling in many dimensions. We present studies demonstrating the viability of this technique on representative HEP datasets, discuss its robustness and systematic behaviour, and compare its performance to standard reweighting-based workflows. This approach offers a scalable path toward improved MC description in modern analyses, where high-dimensional observable spaces are increasingly the norm.