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 (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. Au + Au collisions with the impact parameter of 0 ⩽ b ⩽ 10 fm at intermediate energies (E _lab = 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 LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM’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.

碰撞参数是重离子碰撞的关键物理量之一，显然可以影响最终状态下的许多可观测值，例如多碎片和集体流动。通常，它不能在实验中直接测量，但可以从最终状态下的可观察值推断得出。人工智能在学习数据的复杂表示形式方面取得了巨大的成功，这使得物理科学领域能够采用新颖的建模和数据处理方法。在本文中，我们采用了人工智能领域中的两种常用算法，即卷积神经网络（CNN）和光梯度增强机（LightGBM），以通过分析横向动量中的质子谱来提高确定碰撞参数的准确性。并逐事件快速地进行。b在中间能量⩽10 FM（Ë _实验室= 0.2-1.0电子伏特/核子）是模拟与相对论量子分子动力学模型，以产生质子光谱数据。发现真实冲击参数与估计的冲击参数之间的平均差可以小于0.1 fm。在这项工作中，LightGBM算法相对于CNN表现出更高的性能。通过使用LightGBM的可视化算法，可以获得横向动量和速度分布的重要特征图，这可能有助于推断重离子实验中的碰撞参数或中心性。