Speaker
Vladimir Bocharnikov
(HSE University)
Description
The Highly Granular Neutron Detector (HGND) is designed for the BM@N experiment to study neutron emission in heavy ion collisions at beam energies up to 4A GeV. This detector allows the identification of neutrons and the reconstruction of their energies using time-of-flight method, facilitating the assessment of neutron yields and azimuthal flow. The challenging neutron energy range of $0.5-4$ GeV and large background contributions require sophisticated reconstruction algorithms. In this contribution, we present a machine learning-based approach to the neutron reconstruction problem and discuss preliminary results of the proposed algorithm.
Primary authors
Vladimir Bocharnikov
(HSE University)
Marina Golubeva
(Institute for Nuclear Research RAS)
Fedor Guber
(INR)
Nikolay Karpushkin
(INR RAS)
Sergey Morozov
(INR/MEPhI)
Peter Parfenov
(JINR, NRNU MEPhI)
Fedor Ratnikov
(NRU Higher School of Economics)
Arseniy Shabanov
Aleksandr Zubankov
(Institute for Nuclear Research of the Russian Academy of Sciences)
