Speaker
Description
Due to the expected increase in LHC data from the HL upgrade it is important to work on the efficiency of MC Event Generators in order to make theoretical predictions with the necessary precision accessible. One part of the calculation that could benefit from improvements is the generation of unweighted parton-level events. While adaptive multi-channel importance sampling combined with the Vegas algorithm is a very effective method for a wide range of scattering processes, it can become inefficient for challenging examples. Normalizing Flows are a recent machine learning development based on neural networks that provide trainable bijective mappings. We propose to use Normalizing Flows as a direct replacement for Vegas. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states. We show that our method is able to achieve higher sampling performance than the traditional method for the simpler examples. Furthermore, we discuss the computational challenges and propose possible improvements that could boost the performance of the method also for more complex examples.
| Affiliation | Georg-August-Universität Göttingen |
|---|---|
| Academic Rank | PhD student |
