Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
This addresses the calibration challenge for next-generation cosmological surveys, offering a cost-effective solution for generating high-quality data, though it is incremental in method.
The paper tackles the problem of calibrating galaxy shape measurements for dark energy studies by using deep conditional generative models to create realistic galaxy images, providing a reliable alternative to expensive observations.
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.