Abstract
Non-Standard-Model parity violation may be occurring in LHC collisions. Any such violation would go unseen, however, as searches for it are not currently performed. One barrier to searches for parity violation is the lack of model-independent methods sensitive to all of its forms. We remove this barrier by demonstrating an effective and model-independent way to search for parity-violating physics at the LHC. The method is data-driven and makes no reference to any particular parity-violating model. Instead, it inspects data to construct sensitive parity-odd event variables (using machine learning tools), and uses these variables to test for parity asymmetry in independent data. We demonstrate the efficacy of this method by testing it on data simulated from the Standard Model and from a non-standard parity-violating model. This result enables the possibility of investigating a variety of previously unexplored forms of parity violation in particle physics.Data and software are shared at https://zenodo.org/record/6827724.
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