Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions

Research output: Contribution to journalArticlepeer-review

Abstract

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single detector (either on the signal in zero-degree calorimeters or on the multiplicity in some semi-central rapidity range). In the present work, we made an attempt to develop an approach for centrality determination that is based on machine-learning techniques and utilizes information from several detector subsystems simultaneously. Different event classifiers are suggested and evaluated for their selectivity power in terms of the number of nucleons-participants and the impact parameter of the collision. Finer centrality resolution may allow to reduce impact from so-called volume fluctuations on physical observables being studied in heavy-ion experiments like ALICE at the LHC and fixed target exper
Original languageEnglish
Pages (from-to)11001
JournalEPJ Web of Conferences
Volume137
DOIs
StatePublished - 2017
EventHeavy Flavour Data Mining workshop - Zurich, Switzerland
Duration: 18 Feb 201620 Feb 2016

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