Speaker
Konrad Helms
Description
Modern approaches to phase-space integration combine well-established Monte Carlo methods with machine learning techniques for importance sampling. Recent progress in generative models in the form of continuous normalizing flows, trained using conditional flow matching, offers the potential to improve the phase-space sampling efficiency significantly.
We present a multi-jet inclusive transformer-based phase-space sampler that leverages insights from lower-dimensional phase-spaces to more efficiently generate points in higher-dimensional phase-spaces.
Significance
A transformer-based method is presented that leverages insights from lower-dimensional phase-spaces to more efficiently generate points in higher-dimensional phase-spaces.