  We propose a data-directed paradigm (DDP) to search for new physics. Focusing
on the data without using simulations, exclusive selections which exhibit
significant deviations from known properties of the standard model can be
identified efficiently and marked for further study. Different properties can
be exploited with the DDP. Here, the paradigm is demonstrated by combining the
promising potential of neural networks (NN) with the common bump-hunting
approach. Using the NN, the resource-consuming tasks of background and
systematic uncertainty estimation are avoided, allowing rapid testing of many
final states with only a minor degradation in the sensitivity to bumps relative
to standard analysis methods.
