Universal New Physics Latent Space

Abstract

We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space.

25 pages, 17 figures

References in corpus (6)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Machine Learning for Anomaly Detection in Particle Physics
• Unravelling Strings at the LHC
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Probing intractable beyond-standard-model parameter spaces armed with Machine Learning