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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this article we describe the development of machine learning models toassist the CLAS12 tracking algorithm by identifying tracks through inferringmissing segments in the drift chambers. Auto encoders are used to reconstructmissing segments from track trajectory. Implemented neural network was able toreliably reconstruct missing segment positions with accuracy of $\approx 0.35$wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$.

Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12