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The impact parameter is one of the crucial physical quantities of heavy-ion collisions, and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the convolutional neural networks (CNN) and light gradient boosting machine (LightBGM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au + Au collisions with the impact parameter of 0 ⩽ b ⩽ 10 fm at intermediate energies (Elab = 0.2–1.0 GeV/nucleon) are simulated with the ultrarelativistic quantum molecular dynamics model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightBGM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightBGM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments. [ABSTRACT FROM AUTHOR]

The collision centrality (or the impact parameter b) in heavy-ion experiments is inferred from final state observables, however, usually b is an input to theoretical calculations. The effects of different methods used for the centrality selection (i.e. total charged multiplicity Mch, total transverse kinetic energy of light charged particles , and the ratio of transverse-to-longitudinal kinetic energy ERAT) on the nuclear stopping and the collective flow are studied in Xe+ Sn collisions at INDRA energies (/nucleon). The study is conducted within the framework of the ultra-relativistic quantum molecular dynamics (UrQMD) model. It is found that the nuclear stopping power RE obtained from events binning with ERAT is larger than the experimental data as well as the results obtained using Mch and. At Ebeam = 50 MeV/nucleon, the elliptic flow from events selected with different methods spreads widely, especially for more central collisions. The elliptic flow from events sorted by ERAT is weakly dependent on the collision centrality. The difference in elliptic flow at mid-rapidity among different centrality filters steadily decreases with increasing beam energy and impact parameter. At 150 MeV/nucleon, the differences in directed and elliptic flows arising from different centrality filters vanish. [ABSTRACT FROM AUTHOR]