Speaker
Description
The NEXT experiment is an international collaboration that searches for the neutrinoless double-beta decay using $^{136}\mathrm{Xe}$. It features an entirely gaseous TPC, which allows for the resolution of individual electron tracks. This opens up the possibility to employ machine learning techniques to distinguish between signal and background events based on their topological signature. In this talk, I will present previous efforts in using convolutional neural networks (CNNs) for event classification in NEXT, as well as ongoing work in trying the same with Graph Neural Networks (GNNs). In an earlier study, CNNs were able to successfully identify electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6-MeV gamma rays from a $^{228}\mathrm{Th}$ calibration source. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. The current work on GNNs, while still in development, aims to surpass these results and move on to data from the upcoming NEXT-100 detector.
| Type of contribution | Talk: 15 minutes. |
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