Signal identification with Kalman Filter towards background-free neutrinoless double beta decay searches in gaseous detectors

Abstract

Particle tracks and differential energy loss measured in high pressure gaseous detectors can be exploited for event identification in neutrinoless double beta decay~($0\nu \beta \beta$) searches. We develop a new method based on Kalman Filter in a Bayesian formalism (KFB) to reconstruct meandering tracks of MeV-scale electrons. With simulation data, we compare the signal and background discrimination power of the KFB method assuming different detector granularities and energy resolutions. Typical background from ${}^{232}$Th and ${}^{238}$U decay chains can be suppressed by another order of magnitude than that in published literatures, approaching the background-free regime. For the proposed PandaX-III experiment, the $0\nu \beta \beta$ search half-life sensitivity at the 90\% confidence level would reach $2.7\times 10^{26}$~yr with 5-year live time, a factor of 2.7 improvement over the initial design target.

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Cited by in corpus (3)

• Reconstruction of the event vertex in the PandaX-III experiment with convolution neural network
• Calibration of a Micromegas-based Gaseous Time Projection Chamber Using Cosmic Ray Muons
• Optimization of convolutional neural networks for background suppression in the PandaX-III experiment