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

One of the central goals of the physics program at the future colliders is toelucidate the origin of electroweak symmetry breaking, including precisionmeasurements of the Higgs sector. This includes a detailed study of Higgs boson(H) pair production, which can reveal the H self-coupling. Since the discoveryof the Higgs boson, a large campaign of measurements of the properties of theHiggs boson has begun and many new ideas have emerged during the completion ofthis program. One such idea is the use of highly boosted and merged hadronicdecays of the Higgs boson ($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$,$\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$)with machine learning methods to improve the signal-to-backgrounddiscrimination. In this white paper, we champion the use of these modes toboost the sensitivity of future collider physics programs to Higgs boson pairproduction, the Higgs self-coupling, and Higgs-vector boson couplings. Wedemonstrate the potential improvement possible at the Future Circular Colliderin hadron mode, especially with the use of graph neural networks.

Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning