ATLAS and CMS experiments employ deep-learning methods in searching for rare processes and long-lived particles.

Reflecting on the boundaries between the analog and digital world and how new machine-learning techniques could affect the design of detectors for future experiments. 

The tracking machine challenge stimulates data scientists and the HEP community to renew core tracking algorithms in preparation of the next generation of particle detectors at the LHC.