Speaker
Benedikt Maier
Description
At the LHC, each bunch crossings is able to create thousands of particles per collisions. Identifying a collision of interest from additional “pileup” collisions is a difficult task, requiring the development of dedicated methods. Commonly used methods are however not scalable to future LHC upgrades, where the average number of interactions will increase by almost an order of magnitude. To tackle this challenge, machine learning methods for pileup mitigation are currently been developed to improve and replace standard algorithms. In this talk, an overview of pileup mitigation methods using machine learning in CMS are described.
| Affiliation | CERN |
|---|
