Speaker
Description
The identification of interesting substructures within jets is an important tool to search for new physics and probe the Standard Model. In this paper, we present \textsc{SHAPER}, a general framework for defining computing shape-based observables, which generalizes the $N$-jettiness from point clusters to any extended shape. This is accomplished by minimizing the $p$-Wasserstein metric between events and parameterized manifolds of energy flows representing idealized shapes, implemented using the dual-potential Sinkhorn approximation for efficient minimization. We show how the geometric language of observables as manifolds can be used to easily define novel event and jet-substructure observables with built-in IRC safety that are useful for physics analyses. We then demonstrate the \textsc{SHAPER} framework by performing an example jet substructure analysis using these new shape-based observables.
