Exploring galaxy evolution with generative models

no code implementations • 3 Dec 2018 • Kevin Schawinski, M. Dennis Turp, Ce Zhang

Methods: By learning a latent space representation of the data, we can use this network to forward model and explore hypotheses in a data-driven way.

Using transfer learning to detect galaxy mergers

no code implementations • 25 May 2018 • Sandro Ackermann, Kevin Schawinski, Ce Zhang, Anna K. Weigel, M. Dennis Turp

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers.

General Classification Transfer Learning

PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light

no code implementations • 23 Mar 2018 • Dominic Stark, Barthelemy Launet, Kevin Schawinski, Ce Zhang, Michael Koss, M. Dennis Turp, Lia F. Sartori, Hantian Zhang, Yiru Chen, Anna K. Weigel

We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT.

Astrophysics of Galaxies Data Analysis, Statistics and Probability