Speaker
Description
Models with dark showers represent one of the most challenging possibilities for new physics at the LHC. One of the most difficult examples is a novel collider signature called a Soft Unclustered Energy Pattern (SUEP), which can arise in certain BSM models with a hidden valley sector that is both pseudo-conformal and strongly coupled over a large range of energy scales. Large-angle emissions are unsuppressed during the showering process, and if the hidden sector hadrons decay hadronically and promptly back into the Standard Model, the result is a high-multiplicity shower of SM final state particles that possess more democratically distributed energies and a much higher degree of isotropy than typically seen in QCD jets. This signature presents significant challenges to model, trigger on and search for, due to high theoretical uncertainties and the lack of isolated hard objects to identify in the detector. We outline an analysis strategy to look for SUEP produced by exotic decays of the Higgs boson, using conventional cuts on event-level observables and employing an autoencoder neural network trained on QCD background as an anomaly detector. We compare this unsupervised approach to a simple cut-and-count strategy as well as supervised machine learning models. We find that our strategy could allow the HL-LHC to exclude branching ratios of Higgs decay to SUEP down to a few percent.
