Structured variational inference for simulating populations of radio galaxies
Structured variational inference for simulating populations of radio galaxies
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DOI：
10.1093/mnras/stab588
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发表时间：
2021-02
影响因子：
4.8
通讯作者：
David J. Bastien;A. Scaife;Hongming Tang;Micah Bowles;Fiona A. M. Porter
中科院分区：
文献类型：
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作者：
David J. Bastien;A. Scaife;Hongming Tang;Micah Bowles;Fiona A. M. Porter
我们提出了一个模型，用于生成邮票图像的合成Fanaroff-Riley I类和II类射电星系适用于模拟未来的无线电调查，如那些正在开发的平方公里阵列。该模型使用一个完全连接的神经网络，通过变分自动编码器和解码器架构来实现结构化变分推理。为了优化自动编码器的潜在空间的维数，我们引入了无线电形态学初始分数（RAMIS），一种用于评估生成图像质量的定量方法，并详细讨论了数据预处理选择如何影响该措施的价值。我们研究了VAE的2D潜在空间，并讨论了如何使用它来控制合成种群的生成，同时还警告说，当用于数据增强时，它可能会导致偏差。
We present a model for generating postage stamp images of synthetic Fanaroff–Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such as those being developed for the Square Kilometre Array. This model uses a fully connected neural network to implement structured variational inference through a variational autoencoder and decoder architecture. In order to optimize the dimensionality of the latent space for the autoencoder, we introduce the radio morphology inception score (RAMIS), a quantitative method for assessing the quality of generated images, and discuss in detail how data pre-processing choices can affect the value of this measure. We examine the 2D latent space of the VAEs and discuss how this can be used to control the generation of synthetic populations, whilst also cautioning how it may lead to biases when used for data augmentation.