This work addresses the estimation of the impact parameter in heavy-ion collisions using simulated data from microchannel plate (MCP) detectors planned for future NICA experiments 1. Neural networks can reconstruct the impact parameter accurately, but their performance depends strongly on the chosen event generator. We compared several approaches: principal component analysis, autoencoders, and naive mixed-dataset training did not yield generator-independent features. We then applied domain-adaptation methods, including domain-adversarial and deep reconstruction neural networks (DRNN). DRNN delivered the best performance, reducing generator bias while preserving sensitivity to the impact parameter, especially for central collisions. This may be a promising way toward generalized algorithms that can be reliably applied to forthcoming experimental data.
Galaktionov et al. (Wed,) studied this question.