Abstract: | Abstract: In the first part of the talk I will discuss strategies to search for new physics in LHC data using wavelet transforms of kinematics distributions and machine learning. Such searches are intended to be sensitive to periodic contributions, for example due to a dense spectrum of KK modes. I will also present a recent implementation of such a search in ATLAS. In the second part I will discuss a search strategy for anomalous jets with displaced vertices. Such jets may arise due to a "dark" QCD-like sector. Since the dark sector parameters are largely unconstrained, and the dynamics of a dark QCD-like theory are difficult to compute or simulate reliably, model-independent, data-based searches for such scenarios are desirable. This can be accomplished with weakly supervised machine learning. |