Cargando…

Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV

The ALICE Collaboration reports a new differential measurement of inclusive jet suppression using pp and Pb–Pb collision data at center-of-mass energy per nucleon–nucleon collision $\sqrt{s_{\rm NN}} = 5.02$ TeV. Charged-particle jets are reconstructed using the anti-$k_{\rm T}$ algorithm with resol...

Descripción completa

Detalles Bibliográficos
Autor principal: The ALICE collaboration
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:http://cds.cern.ch/record/2851087
Descripción
Sumario:The ALICE Collaboration reports a new differential measurement of inclusive jet suppression using pp and Pb–Pb collision data at center-of-mass energy per nucleon–nucleon collision $\sqrt{s_{\rm NN}} = 5.02$ TeV. Charged-particle jets are reconstructed using the anti-$k_{\rm T}$ algorithm with resolution parameters $R$ = 0.2, 0.3, 0.4, 0.5, and 0.6 in pp collisions and $R$ = 0.2, 0.4, 0.6 in central (0–10\%), semi-central (30–50\%), and peripheral (60–80\%) Pb–Pb collisions. The analysis uses a novel approach based on machine learning to mitigate the influence of jet background in central heavy-ion collisions, which enables measurements of inclusive jet suppression for jet $p_{\rm T} \ge 40$ GeV/$c$ in central collisions at a resolution parameter of $R$ = 0.6. This is the lowest value of jet $p_{\rm T}$ achieved for inclusive jet measurements at $R$ = 0.6 at the LHC, and is an important step for discriminating different models of jet quenching in the quark-gluon plasma. The transverse momentum spectra, nuclear modification factors, and derived cross section and nuclear modification factor ratios for different jet resolution parameters of charged-particle jets are presented and compared to model predictions. A mild dependence of the nuclear modification factor ratios on collision centrality and resolution parameter is observed. The results are compared to a variety of jet quenching models with varying levels of agreement, demonstrating the effectiveness of this observable to discriminate between models.