Data-Directed Search for New Physics based on Symmetries of the SM

Abstract

We propose exploiting symmetries (exact or approximate) of the Standard Model (SM) to search for physics Beyond the Standard Model (BSM) using the data-directed paradigm (DDP). Symmetries are very powerful because they provide two samples that can be compared without requiring simulation. Focusing on the data, exclusive selections which exhibit significant asymmetry can be identified efficiently and marked for further study. Using a simple and generic test statistic which compares two matrices already provides good sensitivity, only slightly worse than that of the profile likelihood ratio test statistic which relies on the exact knowledge of the signal shape. This can be exploited for rapidly scanning large portions of the measured data, in an attempt to identify regions of interest. Weakly supervised Neural Networks could be used for this purpose as well.

References in corpus (13)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Test of lepton universality in beauty-quark decays
• Classification without labels: Learning from mixed samples in high energy physics
• Extending the Bump Hunt with Machine Learning
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• Asymmetric lepton-flavor violating Higgs decays
• Symmetry meets AI
• MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• A Data-Directed Paradigm for BSM searches: the bump-hunting example

Cited by in corpus (1)

• Anomaly Detection under Coordinate Transformations