Automatic anomaly detection in high energy collider data

We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics signatures.