论文标题
21厘米层析成像样品生成和参数推断的统一框架，并逐渐生长
A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs
论文作者
论文摘要
鉴于所涉及的天体物理过程范围以及可能探测的探测的高维参数空间，创建一系列复杂且计算量的任务是从回报时期（EOR）创建21cm亮度温度信号的数据库是一项复杂且计算昂贵的任务。我们利用特定类型的神经网络，一种逐渐增长的生成对抗网络（PGGAN），在EOR期间生成21cm亮度温度的逼真的断层扫描图像，涵盖了改变X射线发射率的连续三维参数空间，Lyman band band band brand band brangitive，硬性和软X射线之间的比例。经过GPU训练的网络以$ \ sim 3'$的分辨率（在笔记本电脑CPU上）生成新样本，并且由此产生的全局21cm信号，功率谱和像素分配功能与培训数据非常吻合，这些数据与21SSD Catalog \ catalog \ citep \ citep \ semelin2017}相吻合。最后，我们展示了如何通过近似贝叶斯计算从21cm层析成像样品中推断参数的相反任务来利用训练有素的PGGAN。
Creating a database of 21cm brightness temperature signals from the Epoch of Reionisation (EoR) for an array of reionisation histories is a complex and computationally expensive task, given the range of astrophysical processes involved and the possibly high-dimensional parameter space that is to be probed. We utilise a specific type of neural network, a Progressively Growing Generative Adversarial Network (PGGAN), to produce realistic tomography images of the 21cm brightness temperature during the EoR, covering a continuous three-dimensional parameter space that models varying X-ray emissivity, Lyman band emissivity, and ratio between hard and soft X-rays. The GPU-trained network generates new samples at a resolution of $\sim 3'$ in a second (on a laptop CPU), and the resulting global 21cm signal, power spectrum, and pixel distribution function agree well with those of the training data, taken from the 21SSD catalogue \citep{Semelin2017}. Finally, we showcase how a trained PGGAN can be leveraged for the converse task of inferring parameters from 21cm tomography samples via Approximate Bayesian Computation.
