Enabling Dark Energy Science with Deep Generative Models of Galaxy
  Images
  Understanding the nature of dark energy, the mysterious force driving the
accelerated expansion of the Universe, is a major challenge of modern
cosmology. The next generation of cosmological surveys, specifically designed
to address this issue, rely on accurate measurements of the apparent shapes of
distant galaxies. However, shape measurement methods suffer from various
unavoidable biases and therefore will rely on a precise calibration to meet the
accuracy requirements of the science analysis. This calibration process remains
an open challenge as it requires large sets of high quality galaxy images. To
this end, we study the application of deep conditional generative models in
generating realistic galaxy images. In particular we consider variations on
conditional variational autoencoder and introduce a new adversarial objective
for training of conditional generative networks. Our results suggest a reliable
alternative to the acquisition of expensive high quality observations for
generating the calibration data needed by the next generation of cosmological
surveys.
