标题： SEDONA-GesaRaT：用于三维超新星模拟的AI加速辐射转移程序 显示英文标题

标题： SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations

摘要： 我们介绍了SEDONA-GesaRaT，这是一种基于蒙特卡罗辐射转移代码SEDONA开发的快速超新星辐射转移模拟代码。 我们使用了一组原子物理神经网络（APNN），这是一种用于非局部热力学平衡（NLTE）原子物理能级分布计算的人工智能（AI）求解器，它在119个一维I型超新星（SN Ia）辐射转移模拟结果上进行了训练和验证，表现出极高的计算速度和准确性。 SEDONA-GesaRaT已被应用于三维SN Ia爆炸模型N100，以进行三维NLTE辐射转移计算。 使用基于积分的技术（IBT），成功地以高信噪比获取了N100模型的空间分辨线性偏振数据立方体。 使用SEDONA-GesaRaT进行三维NLTE光谱偏振模拟的整体计算成本仅为$\sim$3000核心小时，而之前的代码只能在类似的计算资源下完成一维NLTE模拟或三维局部热力学平衡（LTE）模拟。 出色的计算效率使得SEDONA-GesaRaT能够用于未来的大规模模拟，系统地研究超新星的内部结构。 显示英文摘要

摘要： We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physics neural networks (APNN), an artificial intelligence (AI) solver for the non-local thermodynamic equilibrium (NLTE) atomic physics level population calculation, which is trained and validated on 119 1-D type Ia supernova (SN Ia) radiative transfer simulation results showing great computation speed and accuracy. SEDONA-GesaRaT has been applied to the 3-D SN Ia explosion model N100 to perform a 3-D NLTE radiative transfer calculation. The spatially resolved linear polarization data cubes of the N100 model are successfully retrieved with a high signal-to-noise ratio using the integral-based technique (IBT). The overall computation cost of a 3-D NLTE spectropolarimetry simulation using SEDONA-GesaRaT is only $\sim$3000 core-hours, while the previous codes could only finish 1-D NLTE simulation, or 3-D local thermodynamic equilibrium (LTE) simulation, with similar computation resources. The excellent computing efficiency allows SEDONA-GesaRaT for future large-scale simulations that systematically study the internal structures of supernovae.