JUNIPR: a framework for unsupervised machine learning in particle physics

By Anders Andreassen, Ilya Feige, C. Frye, M. Schwartz. 2018

146 citations recorded by the source corpus.

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. In addition to making unsupervised learning tractable, this design actually alleviates existing tensions between performance and interpretability. We call the framework Junipr: “Jets from UNsupervised Interpretable PRobabilistic models”. In this approach, the set of particle momenta composing a jet are clustered into a binary tree that the neural network examines sequentially. Training is unsupervised and unrestricted: the network could decide that the data bears little correspondence to the chosen tree structure. However, when there is a correspondence, the network’s output along the tree has a direct physical interpretation. Junipr models can perform discrimination tasks, through the statistically optimal likelihood-ratio test, and they permit visualizations of discrimination power at each branching in a jet’s tree. Additionally, Junipr models provide a probability distribution from which events can be drawn, providing a data-driven Monte Carlo generator. As a third application, Junipr models can reweight events from one (e.g. simulated) data set to agree with distributions from another (e.g. experimental) data set.

Subfields: Artificial Intelligence, Nuclear and High Energy Physics, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. I. Moult
  2. Benjamin Nachman
  3. A. Larkoski
  4. Cms Collaboration

In Nuclear and High Energy Physics

  1. I. Moult
  2. Benjamin Nachman
  3. A. Larkoski
  4. G. Soyez
  5. Jesse Thaler
  6. E. Metodiev
  7. Cms Collaboration
  8. Patrick T. Komiske

In AI for Physical Sciences

  1. I. Moult
  2. Benjamin Nachman
  3. A. Larkoski

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