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Understanding strongly correlated quantum many-body states is one of the most thought-provoking challenges in modern research. For example, the Hubbard model, describing strongly correlated electrons in solids, still contains fundamental open questions on its phase diagram. In this work we realize the Hubbard Hamiltonian and search for specific patterns within many individual images of realizations of strongly correlated ultracold fermions in an optical lattice. Upon doping a cold-atom antiferromagnet we find signatures of geometric strings, entities suggested to explain the relationship between hole motion and spin order, in both pattern-based and conventional observables. Our results demonstrate the potential for pattern recognition and more advanced computational algorithms including machine learning to provide key insights into cold-atom quantum many-body systems.

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