Particle tracks and differential energy loss measured in high pressure gaseous detectors can be exploited for event identification in neutrinoless double beta decay (0νββ) 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 232Th and 238U 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 0νββ search half-life sensitivity at the 90% confidence level would reach 2.7× 1026 yr with 5-year live time, a factor of 2.7 improvement over the initial design target.

Natural language understanding tasks require a comprehensive understanding of natural language and further reasoning about it, on the basis of holistic information at different levels to gain comprehensive knowledge. In recent years, pre-trained language models (PrLMs) have shown impressive performance in natural language understanding. However, they rely mainly on extracting context-sensitive statistical patterns without explicitly modeling linguistic information, such as semantic relationships entailed in natural language. In this work, we propose EventBERT, an event-based semantic representation model that takes BERT as the backbone and refines with event-based structural semantics in terms of graph convolution networks. EventBERT benefits simultaneously from rich event-based structures embodied in the graph and contextual semantics learned in pre-trained model BERT. Experimental results on the GLUE benchmark show that the proposed model consistently outperforms the baseline model.