期刊
EUROPEAN PHYSICAL JOURNAL C
卷 79, 期 2, 页码 -出版社
SPRINGER
DOI: 10.1140/epjc/s10052-019-6607-9
关键词
-
资金
- Department of Energy [DE-SC0013607]
- Harvard Data Science Initiative
- ASI Data Science
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据