期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 503, 期 3, 页码 3351-3370出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab588
关键词
methods: statistical; surveys; radio continuum: galaxies
资金
- Science and Technology Facilities Council (STFC)
- Newton Fund through the Development in Africa through Radio Astronomy (DARA) Big Data program [ST/R001898/1]
- Alan Turing Institute AI Fellowship [EP/V030302/1]
- University of Manchester STFC Centre for Doctoral Training (CDT) in Data Intensive Science [ST/P006795/1]
- STFC
- IBM through the iCASE studentship [ST/P006795/1]
The study presents a model for generating postage stamp images of radio galaxies, optimizing the model through improved autoencoders and data preprocessing.
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.
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