MadNIS and the Road to MadGraph7

• Ramon Winterhalder (Università degli Studi di Milano)

High-precision simulations based on first principles are a cornerstone of any modern physics research. For instance, as we approach the HL-LHC era, there is an ever-increasing demand for both accuracy and speed in simulations. Modern Machine Learning (ML) techniques are emerging as a beacon of hope, potentially diminishing the limitations of current methodologies and opening doors to uncharted territory in the parameter space. In this presentation, I will first explain the basic principles of machine learning and highlight current LHC event generation methodologies and their bottlenecks. Afterwards, I will delve into the MadNIS framework and illustrate how modern ML techniques can alleviate these limitations. In particular, I will present recent advancements in neural importance sampling, summarize the developments for differentiable event generators, and outline the future of MadGraph.