Speaker
Dmitry Baranov
(JINR)
Description
The talk presents the first experience of applying deep neural networks to simulate the responses of microstrip GEM detectors used in the BM@N experiment at the NICA accelerator complex (JINR). The preparation of the training dataset based on data obtained using classical Monte-Carlo simulation methods implemented in the BMNROOT software is discussed. The advantages of the neural network approach are considered, particularly the reduction in detector response generation time compared to traditional methods. The quality of the generated responses is also compared with reference simulations.
Primary author
Dmitry Baranov
(JINR)
