Deep Learning-Based Hole-Boring Radiation Pressure Ion Acceleration Modeling

Abstract

Laser-driven ion acceleration has potential applications in high energy density matter, ion beam-driven fast ignition, beam target neutron source and warm dense matter heating and etc. Ultrashort relativistic lasers interacting with solid targets can generate ion beams with energies up to several hundreds of MeV, and the quality of the ion beams strongly depends on the interaction parameters of the laser and the targets. Developments in deep learning can provide new methods in the analysis of relationship between parameters in physics systems, which can significantly reduce the computational and experimental cost. In this paper, a continuous mapping model of ion peak and cutoff energies is developed based on a fully connected neural network(FCNN). In the model, the dataset is composed of nearly 400 sets of particle simulations of laser-driven solid targets, and the input parameters are laser intensity, target density, target thickness and ion mass. The model obtains the parameter analysis results in a large range of values with sparser parameter taking values, which greatly reduces the computational effort of sweeping the parameters in a large range of multi-dimensional parameters. Based on the results of this model mapping, the correction formula for the ion peak energy over ion mass is obtained. Furthermore, the ratio of ion cutoff energy and peak energy of each set of particle simulation is calculated. Repeating the same training process of ion peak energy and cutoff energy, the continuous mapping model of energy ratio is developed. According to the energy ratio model mapping results, the quantitative description of the relationship between ion cutoff energy and peak energy is realized, and the fitting formula for the cutoff energy of the Hole-Boring Radiation Pressure Acceleration (HB-RPA) mechanism is obtained, which can provide an important reference for the laser-driven ion acceleration experiments design.