A Framework for Finding Anomalous Objects at the LHC

Abstract

Search for new physics events at the LHC mostly rely on the assumption that the events are characterized in terms of standard-reconstructed objects such as isolated photons, leptons, and jets initiated by QCD-partons. While such strategy works for a vast majority of physics beyond the standard model scenarios, there are examples aplenty where new physics give rise to anomalous objects (such as collimated and equally energetic particles, decays due to long lived particles etc.) in the detectors, which can not be classified as any of the standard-objects. Varied methods and search strategies have been proposed, each of which is trained and optimized for specific models, topologies, and model parameters. Further, as LHC keeps excluding all expected candidates for new physics, the need for a generic method/tool that is capable of finding the unexpected can not be understated. In this paper, we propose one such method that relies on the philosophy that all anomalous objects are $\it{not}$ standard-objects. The anomaly finder, we suggest, simply is a collection of vetoes that eliminate all standard-objects up to a pre-determined acceptance rate. Any event containing at least one anomalous object (that passes all these vetoes), can be identified as a candidate for new physics. Subsequent offline analyses can determine the nature of the anomalous object as well as of the event, paving a robust way to search for these new physics scenarios in a model-independent fashion. Further, since the method relies on learning only the standard-objects, for which control samples are readily available from data, one can build the analysis in an entirely data-driven way.