Speaker
Description
Next-to-next-to-leading-order QCD calculations are essential for precision collider physics, but their computational cost is often dominated by inefficient phase-space integration. In this talk, I will present the application of neural importance sampling to all contributions entering an NNLO QCD calculation of gluonic top-quark pair production within the STRIPPER subtraction framework. The approach compares discrete coupling-layer and continuous normalizing flows, with the samplers conditioned on sector and helicity information. A central improvement is the stratification of signed integrands into positive and negative components, followed by separate optimization of the corresponding sampling densities. The resulting models substantially reduce event-weight variances, improve unweighting efficiencies, and reproduce differential distributions consistently with conventional integration methods. When integrand evaluation dominates the runtime, the computational cost of reaching a fixed statistical precision is reduced by up to a factor of eight, demonstrating the potential of flow-based sampling for future high-precision collider calculations.