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

We introduce DeeLeMa, a deep learning-based network for the analysis ofenergy and momentum in high-energy particle collisions. This novel approach isspecifically designed to address the challenge of analyzing collision eventswith multiple invisible particles, which are prevalent in many high-energyphysics experiments. DeeLeMa is constructed based on the kinematic constraintsand symmetry of the event topologies. We show that DeeLeMa can robustlyestimate mass distribution even in the presence of combinatorial uncertaintiesand detector smearing effects. The approach is flexible and can be applied tovarious event topologies by leveraging the relevant kinematic symmetries. Thiswork opens up exciting opportunities for the analysis of high-energy particlecollision data, and we believe that DeeLeMa has the potential to become avaluable tool for the high-energy physics community.

DeeLeMa: Missing information search with Deep Learning for Mass estimation