Inpainting hydrodynamical maps with deep learning
  From 1,000 hydrodynamic simulations of the CAMELS project, each with a
different value of the cosmological and astrophysical parameters, we generate
15,000 gas temperature maps. We use a state-of-the-art deep convolutional
neural network to recover missing data from those maps. We mimic the missing
data by applying regular and irregular binary masks that cover either $15\%$ or
$30\%$ of the area of each map. We quantify the reliability of our results
using two summary statistics: 1) the distance between the probability density
functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the
2D power spectrum. We find an excellent agreement between the model prediction
and the unmasked maps when using the power spectrum: better than $1\%$ for
$k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic
offset of $\sim5\%$ when covering $15\%$ of the maps while the results become
unreliable when $30\%$ of the data is missing. The observed KS-test p-values
favor the null hypothesis that the reconstructed and the ground-truth maps are
drawn from the same underlying distribution when irregular masks are used. For
regular-shaped masks on the other hand, we find a strong evidence that the two
distributions do not match each other. Finally, we use the model, trained on
gas temperature maps, to perform inpainting on maps from completely different
fields such as gas mass, gas pressure, and electron density and also for gas
temperature maps from simulations run with other codes. We find that visually,
our model is able to reconstruct the missing pixels from the maps of those
fields with great accuracy, although its performance using summary statistics
depends strongly on the considered field.
