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Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector
CMS Collaboration
Physics and Astronomy (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
6
Scopus citations
Overview
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Dive into the research topics of 'Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector'. Together they form a unique fingerprint.
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Keyphrases
CMS Detector
100%
Invariant Mass
100%
End-to-end Deep Learning
100%
High Energy Physics
50%
Machine Learning
50%
Reconstruction Method
50%
Two-photon
50%
Lorentz
50%
Training Methods
50%
Novel Technique
50%
Particle Properties
50%
Rule-based
50%
Collision Data
50%
Detector Data
50%
Scalar Particles
50%
Minimally Processed
50%
Physics Analysis
50%
Photon Pairs
50%
Deep Learning Strategies
50%
Mass-balance Reconstruction
50%
Particle Reconstruction
50%
Lorentz Boost
50%
Computer Science
Deep Learning Method
100%
Bypass Technique
50%
Machine Learning
50%
Learning System
50%
Physics
Deep Learning Method
100%
Particle Physics
50%
Machine Learning
50%
Engineering
Deep Learning Method
100%
Learning System
50%
Material Science
Particle Property
100%