Using the Wasserstein Distance to Validate a Background Model in XENONnT

Detecting a dark matter particle would not only confirm its existence but also deepen our understanding of the fundamental constituents of matter and the structure of the universe. Directly detecting dark matter requires effective background-signal discrimination. One such background arises in liquid xenon-based detectors from double electron capture (DEC) events. Models have been developed to describe the DEC background in the XENONnT detector, and in this talk I investigate whether the Wasserstein distance as a Goodness-of-Fit test is robust against mismodeling amid low statistics. Overall, relative to other tests, such as the well-known χ² test, the Wasserstein distance has shown to be a more powerful test in specific regions of interest.