  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, completely general, and can be used to derive sensitive,
previously unknown, event variables for other, more complex event topologies.
