Speaker
Description
This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model achieves an energy resolution of $<1\%$, while the semi-supervised models achieve energy resolutions of $\sim 1\%$, and the unsupervised model performance is $\sim1.5\%$. This work is evidence that machine learning denoising can improve energy resolution compared to traditional algorithms, even when experimentalists lack perfect \textit{a priori} knowledge of the signals. Such models provide a realistic path toward next-generation sensitivity in $0\nu\beta\beta$ searches. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-2018730.
| Contribution types | Standard talk (20min + 5min Q/A) |
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