Inpainting hydrodynamical maps with deep learning

Understand

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.