English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Convolutional neural networks (CNNs) are widely used state-of-the-artcomputer vision tools that are becoming increasingly popular in high energyphysics. In this paper, we attempt to understand the potential of CNNs forevent classification in the NEXT experiment, which will search for neutrinolessdouble-beta decay in $^{136}$Xe. To do so, we demonstrate the usage of CNNs forthe identification of electron-positron pair production events, which exhibit atopology similar to that of a neutrinoless double-beta decay event. Theseevents were produced in the NEXT-White high-pressure xenon TPC using 2.6-MeVgamma rays from a $^{228}$Th calibration source. We train a network on MonteCarlo-simulated events and show that, by applying on-the-fly data augmentation,the network can be made robust against differences between simulation and data.The use of CNNs offer significant improvement in signal efficiency/backgroundrejection when compared to previous non-CNN-based analyses.

Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment