A Simulation Driven Deep Learning Approach for Separating Mergers and
  Star Forming Galaxies: The Formation Histories of Clumpy Galaxies in all the
  CANDELS Fields
  Being able to distinguish between galaxies that have recently undergone major
merger events, or are experiencing intense star formation, is crucial for
making progress in our understanding of the formation and evolution of
galaxies. As such, we have developed a machine learning framework based on a
convolutional neural network (CNN) to separate star forming galaxies from
post-mergers using a dataset of 160,000 simulated images from IllustrisTNG100
that resemble observed deep imaging of galaxies with Hubble. We improve upon
previous methods of machine learning with imaging by developing a new approach
to deal with the complexities of contamination from neighbouring sources in
crowded fields and define a quality control limit based on overlapping sources
and background flux. Our pipeline successfully separates post-mergers from star
forming galaxies in IllustrisTNG $80\%$ of the time, which is an improvement by
at least 25\% in comparison to a classification using the asymmetry ($A$) of
the galaxy. Compared with measured S\'ersic profiles, we show that star forming
galaxies in the CANDELS fields are predominantly disc-dominated systems while
post-mergers show distributions of transitioning discs to bulge-dominated
galaxies. With these new measurements, we trace the rate of post-mergers among
asymmetric galaxies in the universe finding an increase from $20\%$ at $z=0.5$
to $50\%$ at $z=2$. Additionally, we do not find strong evidence that the
scattering above the Star Forming Main Sequence (SFMS) can be attributed to
major post-mergers. Finally, we use our new approach to update our previous
measurements of galaxy merger rates $\mathcal{R} = 0.022 \pm 0.006 \times
(1+z)^{2.71\pm0.31}$
