Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using
  Machine Learning
  A significant fraction of high redshift star-forming disc galaxies are known
to host giant clumps, whose nature and role in galaxy evolution are yet to be
understood. In this work we first present a new method based on neural networks
to detect clumps in galaxy images. We use this method to detect clumps in the
rest-frame optical and UV images of a complete sample of $\sim1500$ star
forming galaxies at $1<z<3$ in the CANDELS survey as well as in images from the
VELA zoom-in cosmological simulations. We show that observational effects have
a dramatic impact on the derived clump properties leading to an overestimation
of the clump mass up to a factor of 10, which highlights the importance of fair
comparisons between observations and simulations and the limitations of current
HST data to study the resolved structure of distant galaxies. After correcting
for these effects with a mixture density network, we estimate that the clump
stellar mass function follows a power-law down to the completeness limit
($10^{7}$ solar masses) with the majority of the clumps being less massive than
$10^9$ solar masses. This is in better agreement with recent gravitational
lensing based measurements. The simulations explored in this work overall
reproduce the shape of the observed clump stellar mass function and clumpy
fractions when confronted under the same conditions, although they tend to lie
in the lower limit of the confidence intervals of the observations. This
agreement suggests that most of the observed clumps are formed in-situ.
