How much information is in a jet?

A bstract Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employ...

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Bibliographic Details
Published in:The journal of high energy physics 2017-06, Vol.2017 (6), p.1-25, Article 73
Main Authors: Datta, Kaustuv, Larkoski, Andrew
Format: Article
Language:English
Subjects:
Abstract machines
Algorithms
Artificial intelligence
Bosons
Classical and Quantum Gravitation
Construction equipment
Decay
Discrimination
Elementary Particles
High energy physics
Jets
Large Hadron Collider
Machine learning
Natural language (computers)
Natural language processing
Object recognition
Particle physics
Physics
Physics and Astronomy
QCD Phenomenology
Quantum chromodynamics
Quantum Field Theories
Quantum Field Theory
Quantum Physics
Regular Article - Theoretical Physics
Relativity Theory
String Theory
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Summary:A bstract Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N -body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.
ISSN:1029-8479
1029-8479
DOI:10.1007/JHEP06(2017)073