Nov 14 – 16, 2018
America/Chicago timezone

JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics (20'+5')

Nov 16, 2018, 11:05 AM
One West (WH1W) (Fermilab)

One West (WH1W)



Anders Andreassen (UC Berkeley)


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.

Primary authors

Anders Andreassen (UC Berkeley) Ilya Feige (ASI Data Science) Christopher Frye (ASI Data Science) Matthew D. Schwartz (Harvard)

Presentation materials