Structured Variational Inference for Simulating Populations of Radio Galaxies. (arXiv:2102.01007v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Bastien_D/0/1/0/all/0/1">David J. Bastien</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Scaife_A/0/1/0/all/0/1">Anna M. M. Scaife</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tang_H/0/1/0/all/0/1">Hongming Tang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bowles_M/0/1/0/all/0/1">Micah Bowles</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Porter_F/0/1/0/all/0/1">Fiona Porter</a>
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 auto-encoder
and decoder architecture. In order to optimise the dimensionality of the latent
space for the auto-encoder 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 2-dimensional 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.
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 auto-encoder
and decoder architecture. In order to optimise the dimensionality of the latent
space for the auto-encoder 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 2-dimensional 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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