Learning Latent Jet Structure

Learning Latent Jet Structure

We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Alloca...

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Bibliographic Details
Main Authors: Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik, Manuel Szewc
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Symmetry
Subjects:
QCD
jet substructure analysis
Bayesian semi-supervised learning
Online Access:https://www.mdpi.com/2073-8994/13/7/1167
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spelling doaj-26f7d9bc1ad6453bb60ea22fcee4953c2021-07-23T14:09:08ZengMDPI AGSymmetry2073-89942021-06-01131167116710.3390/sym13071167Learning Latent Jet StructureBarry M. Dillon0Darius A. Faroughy1Jernej F. Kamenik2Manuel Szewc3Institut fur Theoretische Physik, Universitat Heidelberg, 69120 Heidelberg, GermanyPhysik-Institut, Universitat Zurich, CH-8057 Zurich, SwitzerlandJozef Stefan Institute, Jamova 39, 1000 Ljubljana, SloveniaInternational Center for Advanced Studies (ICAS), UNSAM & CONICET 25 de Mayo y Francia, 1650 Buenos Aires, ArgentinaWe summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>t</mi><mover><mi>t</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula> jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.https://www.mdpi.com/2073-8994/13/7/1167QCDjet substructure analysisBayesian semi-supervised learning
collection DOAJ
language English
format Article
sources DOAJ
author Barry M. Dillon
Darius A. Faroughy
Jernej F. Kamenik
Manuel Szewc
spellingShingle Barry M. Dillon
Darius A. Faroughy
Jernej F. Kamenik
Manuel Szewc
Learning Latent Jet Structure
Symmetry
QCD
jet substructure analysis
Bayesian semi-supervised learning
author_facet Barry M. Dillon
Darius A. Faroughy
Jernej F. Kamenik
Manuel Szewc
author_sort Barry M. Dillon
title Learning Latent Jet Structure
title_short Learning Latent Jet Structure
title_full Learning Latent Jet Structure
title_fullStr Learning Latent Jet Structure
title_full_unstemmed Learning Latent Jet Structure
title_sort learning latent jet structure
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2021-06-01
description We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>t</mi><mover><mi>t</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula> jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.
topic QCD
jet substructure analysis
Bayesian semi-supervised learning
url https://www.mdpi.com/2073-8994/13/7/1167
work_keys_str_mv AT barrymdillon learninglatentjetstructure
AT dariusafaroughy learninglatentjetstructure
AT jernejfkamenik learninglatentjetstructure
AT manuelszewc learninglatentjetstructure
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