Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Understand

The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators.

• Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications.
• Here we present a novel two-staged unweighting procedure that makes use of a neural-network surrogate for the full event weight.
• The algorithm can significantly accelerate the unweighting process, while it still guarantees unbiased sampling from the correct target distribution.