Abstract:

Conventional methods for searching for new physics at the LHC have
mostly been “top-down”: starting from a specific model, searches are
designed and optimized to have the best sensitivity to that model.
Despite hundreds of conventional new physics searches at the LHC, none
have turned up any hint of new physics. Maybe it’s time to admit that we
don’t know what we’re looking for.

Breakthroughs in modern deep learning have the potential to
revolutionize how we search for new physics at the LHC. In particular,
techniques borrowed from unsupervised machine learning could enable us
to search for new physics in a largely model-agnostic way. In this talk
I will review some promising recent proposals in this direction. These
proposed search strategies could complement more conventional methods by
finding surprising signals that were not anticipated by any model,
ensuring that we leave no stone unturned in the hunt for new physics at
the LHC.