Speaker
Description
Searches for Charge-Parity (CP) violation in multibody decays provide a powerful probe of physics beyond the Standard Model, particularly in scenarios where interference effects and other final-state interactions can potentially generate localised asymmetries across the decay phase space, which may be significantly larger than the corresponding phase-space–integrated effects. In decays with high-dimensional phase spaces, traditional model-independent approaches often rely on low-dimensional representations, which might reduce their sensitivity.
We present a simple and fully model-independent machine-learning-based approach to search for CP violation in decay phase spaces of arbitrary dimension. A classifier is trained to distinguish particle from antiparticle decays using the full phase-space information, and two-sample test statistics constructed from the classifier output are used to test the CP symmetry hypothesis. The method is validated using simulated samples, and its sensitivity is compared to established model-independent techniques.
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