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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

We investigate the feasibility of using deep learning techniques, in the formof a one-dimensional convolutional neural network (1D-CNN), for the extractionof signals from the raw waveforms produced by the individual channels of liquidargon time projection chamber (LArTPC) detectors. A minimal generic LArTPCdetector model is developed to generate realistic noise and signal waveformsused to train and test the 1D-CNN, and evaluate its performance on low-levelsignals. We demonstrate that our approach overcomes the inherent shortcomingsof traditional cut-based methods by extending sensitivity to signals with ADCvalues below their imposed thresholds. This approach exhibits great promise inenhancing the capabilities of future generation neutrino experiments like DUNEto carry out their low-energy neutrino physics programs.

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept