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Sensitivity Studies Exploiting Multivariate Techniques

  • Nicolas Maximilian KöhlerEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

The classification between signal-like and background like-events exploiting discriminating variables is considered as the most intuitive method in searches for new particles or in measurements at the LHC. It allows for a good understanding and interpretation of the underlying physics processes as well as the possibility for theoreticians to exploit the event selection for further studies. However, the performance of event classification based on discriminating variables can suffer from correlations amongst a subset of those variables which complicate the definition of the ideal selection requirements in order to thoroughly separate signal-like from background-like events. The chapter describes the application of machine learning techniques in the event classification.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Experimental PhysicsCERNMeyrinSwitzerland

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