Ultra-Fast Event Generation using Modern Neural Networks

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Particle physics is undergoing a rapid transformation driven by contemporary data science, including modern machine learning (ML) techniques. Applications of such methods are driven by a unique combination of fundamental physics questions with fast first-principle simulations, vast datasets, and full uncertainty control. The keystone in this development and the goal of this proposal are ML-based simulation and analysis tools, combining event generators with simulation-based inference methods. Our ambition is to build the first ML- based matrix-element event generator, opening the era of ultra-fast event generation in high-energy physics, with a vast range of possible applications in phenomenology, data analysis, and interpretation.