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Author |
Aguilar, A.C.; De Soto, F.; Ferreira, M.N.; Papavassiliou, J.; Pinto-Gomez, F.; Roberts, C.D.; Rodriguez-Quintero, J. |
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Title |
Schwinger mechanism for gluons from lattice QCD |
Type |
Journal Article |
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Year |
2023 |
Publication  |
Physics Letters B |
Abbreviated Journal |
Phys. Lett. B |
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Volume |
841 |
Issue |
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Pages |
137906 - 8pp |
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Keywords |
Continuum Schwinger function methods; Emergence of mass; Gluons; Lattice Schwinger function methods; Quantum chromodynamics; Schwinger mechanism of gauge boson mass; generation |
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Abstract |
Continuum and lattice analyses have revealed the existence of a mass-scale in the gluon two-point Schwinger function. It has long been conjectured that this expresses the action of a Schwinger mechanism for gauge boson mass generation in quantum chromodynamics (QCD). For such to be true, it is necessary and sufficient that a dynamically-generated, massless, colour-carrying, scalar gluon+gluon correlation emerges as a feature of the dressed three-gluon vertex. Working with results on elementary Schwinger functions obtained via the numerical simulation of lattice-regularised QCD, we establish with an extremely high level of confidence that just such a feature appears; hence, confirm the conjectured origin of the gluon mass scale. |
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Address |
[Aguilar, A. C.] Univ Estadual Campinas, Inst Phys Gleb Wataghin, UNICAMP, BR-13083859 Campinas, SP, Brazil, Email: cristina.aguilar@unicamp.br; |
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Publisher |
Elsevier |
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Language |
English |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0370-2693 |
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Notes |
WOS:000984221700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5530 |
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Author |
Kasieczka, G. et al; Sanz, V. |
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Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
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Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
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Volume |
84 |
Issue |
12 |
Pages |
124201 - 64pp |
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Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
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Abstract |
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
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Address |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
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Corporate Author |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0034-4885 |
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Expedition |
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Conference |
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Notes |
WOS:000727698500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5039 |
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