Using machine-learning methods based on self-organising Kohonen maps, the results of numerical simulation of the acceleration of electrons during the interaction of high-power laser radiation with plasma are analysed and classified. The particle-in-cell (PIC) method is used to simulate the interaction in a wide range of parameters (laser intensity and plasma concentration). For each set of parameters, the spectrum of accelerated electrons is found, based on which the charge, average energy, and relative energy spread of accelerated electrons are calculated. Using the obtained values as input parameters of the map, the classification of various acceleration regimes is performed. The developed scheme can be used to identify the optimal acceleration regimes under more realistic conditions, considering a larger number of parameters.

使用基于自组织 Kohonen 图的机器学习方法，对高功率激光辐射与等离子体相互作用过程中电子加速度的数值模拟结果进行了分析和分类。细胞内粒子 (PIC) 方法用于模拟各种参数（激光强度和血浆浓度）下的相互作用。对于每组参数，都会找到加速电子的光谱，基于此计算加速电子的电荷、平均能量和相对能量分布。使用获得的值作为地图的输入参数，执行各种加速状态的分类。考虑到大量参数，开发的方案可用于在更现实的条件下确定最佳加速状态。