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

Searching for new physics in large data sets needs a balance between twocompeting effects---signal identification vs background distortion. In thiswork, we perform a systematic study of both single variable and multivariatejet tagging methods that aim for this balance. The methods preserve the shapeof the background distribution by either augmenting the training procedure orthe data itself. Multiple quantitative metrics to compare the methods areconsidered, for tagging 2-, 3-, or 4-prong jets from the QCD background. Thisis the first study to show that the data augmentation techniques of Planing andPCA based scaling deliver similar performance as the augmented trainingtechniques of Adversarial NN and uBoost, but are both easier to implement andcomputationally cheaper.

Mass Agnostic Jet Taggers