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We propose a data-directed paradigm (DDP) to search for new physics. Focusingon the data without using simulations, exclusive selections which exhibitsignificant deviations from known properties of the standard model can beidentified efficiently and marked for further study. Different properties canbe exploited with the DDP. Here, the paradigm is demonstrated by combining thepromising potential of neural networks (NN) with the common bump-huntingapproach. Using the NN, the resource-consuming tasks of background andsystematic uncertainty estimation are avoided, allowing rapid testing of manyfinal states with only a minor degradation in the sensitivity to bumps relativeto standard analysis methods.

A Data-Directed Paradigm for BSM searches: the bump-hunting example