Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions (abstract for Heavy Flavour Data Mining workshop)

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике

Выдержка

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the 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 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. The authors acknowledge Saint-Petersburg State University for a research grant 11.38.242.2015.
Язык оригиналане определен
Название основной публикацииBook of Abstracts of Heavy Flavour Data Mining workshop
СостояниеОпубликовано - 2016

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title = "Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions (abstract for Heavy Flavour Data Mining workshop)",
abstract = "Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the 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 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. The authors acknowledge Saint-Petersburg State University for a research grant 11.38.242.2015.",
author = "Igor Altsybeev and Vladimir Kovalenko",
year = "2016",
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Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions (abstract for Heavy Flavour Data Mining workshop). / Altsybeev, Igor; Kovalenko, Vladimir.

Book of Abstracts of Heavy Flavour Data Mining workshop. 2016.

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике

TY - CHAP

T1 - Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions (abstract for Heavy Flavour Data Mining workshop)

AU - Altsybeev, Igor

AU - Kovalenko, Vladimir

PY - 2016

Y1 - 2016

N2 - Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the 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 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. The authors acknowledge Saint-Petersburg State University for a research grant 11.38.242.2015.

AB - Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the 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 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. The authors acknowledge Saint-Petersburg State University for a research grant 11.38.242.2015.

M3 - статья в сборнике

BT - Book of Abstracts of Heavy Flavour Data Mining workshop

ER -