3 papers
astro-ph.GA2018
Exploring galaxy evolution with generative models
Kevin Schawinski, M. Dennis Turp, Ce Zhang
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypoth…
astro-ph.IM2018
Using transfer learning to detect galaxy mergers
Sandro Ackermann, Kevin Schawinski, Ce Zhang +2
We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in…
astro-ph.GA2018
PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light
Dominic Stark, Barthelemy Launet, Kevin Schawinski +7
The study of unobscured active galactic nuclei (AGN) and quasars depends on the reliable decomposition of the light from the AGN point source and the extended host galaxy light. Th…