End-to-end physics reconstruction or what if we can't reconstruct new physics at the CERN LHC?
Rule-based particle reconstruction algorithms underpin almost all major physics results of the CMS collaboration at the CERN LHC. Yet no new physics has been discovered since the Higgs boson in 2012 despite grounds for the contrary. Could the limitations of rule-based particle reconstruction algorithms perhaps be responsible for the lack of new physics results? In this talk, we consider models of exotic physics whose experimental signatures would go unnoticed in the CMS detector due to the limitations of current rule-based particle reconstruction algorithms. We then propose a novel end-to-end physics reconstruction technique and show how it is able to probe such exotic regimes that were once thought to be inaccessible. The technique leverages deep learning methods to train directly on "raw" detector data in order to bypass bottlenecks in current rule-based algorithms. We show how this new technique allows a number of never-before-seen measurements to be performed, and speculate on others yet it may enable.