Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions
This work addresses centrality determination for heavy-ion experiments like ALICE and NA61/SHINE, potentially reducing volume fluctuations in physical observables, but it is incremental as it builds on existing methods by integrating multiple detectors.
The authors tackled the problem of estimating centrality in proton-nucleus and nucleus-nucleus collisions, which is not directly measurable, by developing a machine-learning approach that uses multiple detector subsystems, resulting in improved selectivity for nucleon participants and impact parameters.
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 experiment NA61/SHINE on SPS.