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

Abstract

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.

References in corpus (2)

• Extending the Bump Hunt with Machine Learning
• Asymmetric lepton-flavor violating Higgs decays

Cited by in corpus (6)

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• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Data-Directed Search for New Physics based on Symmetries of the SM
• Non-resonant Anomaly Detection with Background Extrapolation
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure