Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events

This review introduces recent developments in the application of image processing, computer vision, and deep neural networks to the analysis and interpretation of particle collision events at the Large Hadron Collider (LHC). The link between LHC data analysis and computer vision techniques relies on...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 762; no. 1; pp. 12035 - 12044
Main Authors: Schwartzman, A., Kagan, M., Mackey, L, Nachman, B., De Oliveira, L.
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.10.2016
Subjects:
Artificial neural networks
Computer architecture
Computer vision
Data analysis
Deep learning
Digital cameras
Digital imaging
Image classification
Image processing
Large Hadron Collider
Machine learning
Neural networks
Particle collisions
Particle physics
Physics
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:This review introduces recent developments in the application of image processing, computer vision, and deep neural networks to the analysis and interpretation of particle collision events at the Large Hadron Collider (LHC). The link between LHC data analysis and computer vision techniques relies on the concept of jet-images, building on the notion of a particle physics detector as a digital camera and the particles it measures as images. We show that state-of-the-art image classification techniques based on deep neural network architectures significantly improve the identification of highly boosted electroweak particles with respect to existing methods. Furthermore, we introduce new methods to visualize and interpret the high level features learned by deep neural networks that provide discrimination beyond physics- derived variables, adding a new capability to understand physics and to design more powerful classification methods at the LHC.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/762/1/012035