Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions. / Altsybeev, Igor; Kovalenko, Vladimir.
в: EPJ Web of Conferences, Том 137, 2017, стр. 11001.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions
AU - Altsybeev, Igor
AU - Kovalenko, Vladimir
PY - 2017
Y1 - 2017
N2 - 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
AB - 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
U2 - 10.1051/epjconf/201713711001
DO - 10.1051/epjconf/201713711001
M3 - Article
VL - 137
SP - 11001
JO - EPJ Web of Conferences
JF - EPJ Web of Conferences
SN - 2100-014X
T2 - Heavy Flavour Data Mining workshop
Y2 - 18 February 2016 through 20 February 2016
ER -
ID: 78167320