In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics.
In this talk, we will present a new framework: JUNIPR, Jets from UNsupervised Interpretable PRobabilistic models, which uses unsupervised learning to learn the intricate high-dimensional contours of the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, JUNIPR is structured intelligently around a leading-order model of the physics underlying the data.
In addition to making unsupervised learning tractable, this design actually alleviates existing tensions between performance and interpretability.
Applications to discrimination, data-driven Monte Carlo generation and reweighting of events will be discussed.