The Lund jet plane

Lund diagrams, a theoretical representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out that they can be created for individual jets through repeated Cambridge/Aachen declustering, providing a powerful visual representation of the r...

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Bibliographic Details
Main Authors: Soyez, Grégory (Author), Salam, Gavin P. (Author), Dreyer, Frederic (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Physics (Contributor)
Format: Article
Language:English
Published: Springer Berlin Heidelberg, 2019-01-04T15:30:11Z.
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Online Access:Get fulltext
LEADER 01949 am a22002173u 4500
001 119851
042 |a dc 
100 1 0 |a Soyez, Grégory  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Physics  |e contributor 
100 1 0 |a Dreyer, Frederic  |e contributor 
700 1 0 |a Salam, Gavin P.  |e author 
700 1 0 |a Dreyer, Frederic  |e author 
245 0 0 |a The Lund jet plane 
260 |b Springer Berlin Heidelberg,   |c 2019-01-04T15:30:11Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/119851 
520 |a Lund diagrams, a theoretical representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out that they can be created for individual jets through repeated Cambridge/Aachen declustering, providing a powerful visual representation of the radiation within any given jet. Concentrating here on the primary Lund plane, we outline some of its analytical properties, highlight its scope for constraining Monte Carlo simulations and comment on its relation with existing observables such as the zg variable and the iterated soft-drop multiplicity. We then examine its use for boosted electroweak boson tagging at high momenta. It provides good performance when used as an input to machine learning. Much of this performance can be reproduced also within a transparent log-likelihood method, whose underlying assumption is that different regions of the primary Lund plane are largely decorrelated. This suggests a potential for unique insight and experimental validation of the features being used by machine-learning approaches. Keywords: Jets, QCD Phenomenology 
520 |a Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Grant P2SKP2-165039) 
520 |a United States. Department of Energy. Office of High Energy and Nuclear Physics (Grant DE-SC-0012567) 
546 |a en 
655 7 |a Article 
773 |t Journal of High Energy Physics