JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics

Understand

In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics.

• To this end, a powerful tool would be a framework for unsupervised learning, where the machine learns 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, an unsupervised network must be structured intelligently, based on a qualitative understanding of the data.
• In this paper, we scaffold the neural network's architecture around a leading-order model of the physics underlying the data.