Neutron Reconstruction in the BM@N Experiment Using Machine Learning

IF 0.6 4区 物理与天体物理 Q4 PHYSICS, PARTICLES & FIELDS Physics of Particles and Nuclei Pub Date : 2024-08-18 DOI:10.1134/s1063779624700400
V. Bocharnikov, D. Derkach, M. Golubeva, F. Guber, S. Morozov, P. Parfenov, F. Ratnikov
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Abstract

At present, new compact highly granular neutron detector is being developed for the BM@N experiment. This detector will be used to identify neutrons, to measure their energies using time-of-flight method, neutron yields and azimuthal flow of neutrons in heavy-ion collisions at beam energies up to 4 A GeV. Application of machine learning techniques and preliminary results of neutron identification and energy reconstruction are discussed. First predictions of the anisotropic flow of neutrons using the DCM-QGSM-SMM model of heavy-ion collisions are shown.

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利用机器学习在 BM@N 实验中重建中子
摘要 目前,正在为 BM@N 实验开发新的紧凑型高颗粒中子探测器。该探测器将用于识别中子,利用飞行时间法测量中子的能量、中子产率以及在束流能量高达4 A GeV的重离子碰撞中子的方位流。讨论了机器学习技术的应用以及中子识别和能量重建的初步结果。使用重离子碰撞的 DCM-QGSM-SMM 模型对中子各向异性流进行了首次预测。
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来源期刊
Physics of Particles and Nuclei
Physics of Particles and Nuclei 物理-物理:粒子与场物理
CiteScore
1.00
自引率
0.00%
发文量
116
审稿时长
6-12 weeks
期刊介绍: The journal Fizika Elementarnykh Chastits i Atomnogo Yadr of the Joint Institute for Nuclear Research (JINR, Dubna) was founded by Academician N.N. Bogolyubov in August 1969. The Editors-in-chief of the journal were Academician N.N. Bogolyubov (1970–1992) and Academician A.M. Baldin (1992–2001). Its English translation, Physics of Particles and Nuclei, appears simultaneously with the original Russian-language edition. Published by leading physicists from the JINR member states, as well as by scientists from other countries, review articles in this journal examine problems of elementary particle physics, nuclear physics, condensed matter physics, experimental data processing, accelerators and related instrumentation ecology and radiology.
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