Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

Understand

The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), 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 (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis.