期刊
JOURNAL OF HIGH ENERGY PHYSICS
卷 -, 期 7, 页码 -出版社
SPRINGER
DOI: 10.1007/JHEP07(2016)069
关键词
Jet substructure; Hadron-Hadron scattering (experiments)
资金
- Stanford Data Science Initiative
- US Department of Energy (DOE) [DE-AC02-76SF00515]
- NSF Graduate Research Fellowship [DGE-4747]
- Stanford Graduate Fellowship
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet image scan out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.
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