Speaker
Description
The XENONnT detector is a dual-phase xenon time projection chamber to search for rare low-energy events. While its main purpose is the direct detection of Dark Matter, XENONnT is also sensitive to neutrino interactions for example from solar 8B neutrinos. To fully utilize the fiducialization and background reduction capabilities of the XENONnT detector, it is important to know the exact position of events in the detector. The event position reconstruction is commonly performed by a combination of different neural networks (NNs). Like most machine learning models, these NNs output a singular point in the output space, here the horizontal plane of the detector. In this talk I will present a modification of the NNs, which changes their output from a singular point to a probability density function (PDF) that spans the complete output space. The resulting PDF can be analyzed to learn about trends and biases in the position reconstruction, ultimately leading to an improved signal to background discrimination: The parameters of the PDF can be used to filter for potentially incorrectly reconstructed events. Additionally, the position uncertainty can be propagated through the full event reconstruction chain, providing a more accurate estimation of systematic uncertainties of the experiment.
| Type of contribution | Talk: 15 minutes. |
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