|DATE||November 09 (Tue), 2021|
|INSTITUTE||University of Toronto|
|TITLE||Unsupervised Hadronic SUEP at the LHC|
|ABSTRACT||Confining dark sectors with pseudo-conformal dynamics produce SUEP, or Soft Unclustered Energy Patterns, at colliders: isotropic dark hadrons with soft and demo- cratic energies. We target the experimental nightmare scenario, SUEPs in exotic Higgs decays, where all dark hadrons decay promptly to SM hadrons. First, we identify three promising observables, the charged particle multiplicity, the event ring isotropy, and the matrix of geometric distances between charged tracks. Their patterns can be exploited through a cut-and-count search, supervised machine learning, or an unsupervised autoen- coder. We find that the HL-LHC will probe exotic Higgs branching ratios at the per-cent level, even without a detailed knowledge of the signal features. Our techniques can be ap- plied to other SUEP searches, especially the unsupervised strategy, which is independent of overly specific model assumptions and the corresponding precision simulations.