Search for clustering of strange quarks in relativistic heavy ion collisions at the LHC

Date

2021-05

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Abstract

Heavy ion collisions at the LHC provide a tool to study the phase transition from hadronic matter to a deconfined phase of quarks and gluons. We investigated whether flavor hierarchy in the QCD transition crossover region could lead to clustering of strangeness. The idea was supported by the enhanced production of strange carrying hadrons in heavy-ion collisions. Kaons are the lightest meson that carry the strange quark and thus they represent around 80% of the total strangeness in the system. We search for clusters of strange quarks in heavy-ion collisions, taking kaons as a proxy, by studying the azimuthal distribution of particles on an event-by-event basis. To find the signatures of clustering, we defined two different observables, probability of event clustering P(E) and Pull distributions. The shape of these distributions are expected to carry the possible clustering signature. We measured the higher moments of these distributions and compared them with the baselines, which have no clustering. These baselines are achieved either through simple statistical considerations of Poissonian particle production, leading to Gaussian signatures, or through analysis of realistic MC event generator, HIJING, that do not carry any mechanism of generated particle cluster or even flavor hierarchy in the particle freeze-out. We find no explicit evidence for azimuthal strangeness, but the detailed analysis allows us to set more stringent thresholds on the clustering probability. A sensitivity study shows that the higher moments (skewness and kurtosis) are more prone to show a signal. At this time, with the presently available dataset and the resulting statistical and systematic uncertainties on the higher moment measurements, we can state that the strangeness clustering probability in central heavy ion collisions at LHC energies is less than 20%.

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Keywords

Heavy-ion, Strange quarks, quark-gluon plasma, Central moments, clustering, fluctuations

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