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

We introduce two new loss functions designed to directly optimise thestatistical significance of the expected number of signal events when trainingneural networks to classify events as signal or background in the scenario of asearch for new physics at a particle collider. The loss functions are designedto directly maximise commonly used estimates of the statistical significance,$s/\sqrt{s+b}$, and the Asimov estimate, $Z_A$. We consider their use in a toySUSY search with 30~fb$^{-1}$ of 14~TeV data collected at the LHC. In the casethat the search for the SUSY model is dominated by systematic uncertainties, itis found that the loss function based on $Z_A$ can outperform the binary crossentropy in defining an optimal search region.

Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders