Speaker
Description
The LHC search programme remains largely model-driven, probing one final state and one model hypothesis at a time, complemented by occasional model-agnostic searches such as those in dijet spectra. We aim to take the next step: probing many final states at once, in an automatic and robust way, using limited resources efficiently to cover large regions of phase space. This combines reusing our knowledge of established models to boost sensitivity with model-agnostic anomaly-detection techniques.
We estimate the total number of searches required to cover everything within our reach, and examine how the look-elsewhere effect limits sensitivity in large-scale automated searches, along with strategies to manage it. Finally, we present a proof-of-concept using Gaussian Process Regression as a robust, sensitive, data-driven background-estimation technique across a broad range of smoothly falling spectra.