Measurement of colour flow using jet-pull observables in $t\bar{t}$ events with the ATLAS experiment at $\sqrt{s} = 13\,\hbox {TeV}$

• Autor(a) principal: ATLAS collaboration (2844 authors)
• Data de Publicação: 2018
• Outros Autores: Aguilar Saavedra, Juan Antonio, Amor Dos Santos, Susana Patricia, Araque Espinosa, Juan Pedro, Castro, Nuno Filipe, Conde Muino, Patricia, Da Cunha Sargedas De Sousa, Mario Jose, Fiolhais, Miguel, Galhardo, Bruno, Gomes, Agostinho, Goncalo, Ricardo, Jorge, Pedro, Machado Miguens, Joana, Maio, Amelia, Maneira, Jose, Oleiro Seabra, Luis Filipe, Onofre, Antonio, Costa Batalha Pedro, Rute, Santos, Helena, Saraiva, Joao, Silva, Jose Manuel, Tavares Delgado, Ademar, Veloso, Filipe, Wolters, Helmut
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.26/27629

Previous studies have shown that weighted angular moments derived from jet constituents encode the colour connections between partons that seed the jets. This paper presents measurements of two such distributions, the jet-pull angle and jet-pull magnitude, both of which are derived from the jet-pull angular moment. The measurement is performed in $t\bar{t}$ events with one leptonically decaying W boson and one hadronically decaying W boson, using $36.1\,\text {fb}^{-1}$ of pp collision data recorded by the ATLAS detector at $\sqrt{s} = 13 \, \text {TeV}$ delivered by the Large Hadron Collider. The observables are measured for two dijet systems, corresponding to the colour-connected daughters of the W boson and the two b-jets from the top-quark decays, which are not expected to be colour connected. To allow the comparison of the measured distributions to colour model predictions, the measured distributions are unfolded to particle level, after correcting for experimental effects introduced by the detector. While good agreement can be found for some combinations of predictions and observables, none of the predictions describes the data well across all observables.