标题： 用于模拟广延空气簇射无线电发射的生成神经网络 显示英文标题

标题： Generative Neural Network for Simulating Radio Emission from Extensive Air Showers

摘要： 利用大型射电阵列进行宇宙射线簇射检测近年来受到了广泛关注。 随着信号建模和微观模拟的显著改进，对入射事件的分析仍严重受限于无线电辐射模拟的计算成本，从而难以解释数据。 在本工作中，我们表明神经网络可以用于模拟此类无线电脉冲。 我们还展示了如何使用这样的神经网络进行$X_\mathrm{max}$重建，同时保持与使用完整的蒙特卡洛 CORSIKA/CoREAS 模拟进行无线电辐射相当的分辨率。 显示英文摘要

摘要： Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by the simulation cost of radio emission to interpret the data. In this work, we show that a neural network can be used for simulating such radio pulses. We also demonstrate how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining comparable resolution to using full Monte-Carlo CORSIKA/CoREAS simulations for radio emission.