https://argonne.zoomgov.com/j/1619891031?pwd=Y2hsTCtwUkZCUlZOMVg1Q0NTTDE0QT09
We discuss methods for discovering new physics by using the substructure of
boosted heavy resonances to reduce light quark and gluon backgrounds.
Here we detail cutting-edge machine learning algorithms that allow for
unprecedented sensitivity in all hadronic searches, and the first physics analyses that take advantage of these algorithms.
We then outline the future of these searches, considering both algorithmic
and hardware advances. The continued success of heavy resonance searches requires high-resolution tracking data, so we look forward to the HLLHC CMS outer tracker upgrade.
We concentrate on PS module prototyping, from the very first working
assemblies to the most recent results.