Amplitude analysis serves as a pivotal tool in hadron spectroscopy,
fundamentally involving a series of likelihood fits to multi-dimensional
experimental distributions. While numerous robust goodness-of-fit tests are
available for low-dimensional scenarios, evaluating goodness-of-fit in
amplitude analysis poses significant challenges. In this work, we introduce a
powerful goodness-of-fit test leveraging a machine-learning-based anomaly
detection method.

Questo articolo esplora i giri e le loro implicazioni.

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