Deep-learned event variables for collider phenomenology
Doojin Kim, Kyoungchul Kong, K. Matchev, Myeonghun Park, Prasanth Shyamsundar
Abstract
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for many typical event topologies in collider physics. Here, we introduce a deep-learning technique to design good event variables, which are sensitive over a wide range of values for the unknown model parameters. We demonstrate that the neural networks trained with our technique on some simple event topologies are able to reproduce standard event variables like invariant mass, transverse mass, and stransverse mass. The method is automatable and completely general and can be used to derive sensitive, previously unknown, event variables for other, more complex event topologies.