A new way to constrain the densities of intra-group medium in groups of
  galaxies with convolutional neural networks
  Ram pressure (RP) can influence the evolution of cold gas content and star
formation rates of galaxies. One of the key parameters for the strength of RP
is the density of intra-group medium ($\rho_{\rm igm}$), which is difficult to
estimate if the X-ray emission from it is too weak to be observed. We propose a
new way to constrain $\rho_{\rm igm}$ through an application of convolutional
neural networks (CNNs) to simulated gas density and kinematic maps galaxies
under strong RP. We train CNNs using $9\times{}10^4$ 2D images of galaxies
under various RP conditions, then validate performance with $10^4$ new test
images. This new method can be applied to real observational data from ongoing
WALLABY and SKA surveys to quickly obtain estimates of $\rho_{\rm igm}$.
Simulated galaxy images have $1.0$ kpc resolution, which is consistent with
that expected from the future WALLABY survey. The trained CNN models predict
the normalised IGM density, $\hat{\rho}_{\rm igm}$ where $0.0 \le
\hat{\rho}_{\rm igm, n} < 10.0$, accurately with root mean squared error values
($\rm RMSE$) of $0.72$, $0.83$ and $0.74$ for the density, kinematic and joined
2D maps, respectively. Trained models are unable to predict the relative
velocity of galaxies with respect to the IGM ($v_{\rm rel}$) precisely, and
struggle to generalise for different RP conditions. We apply our CNNs to the
observed HI column density map of NGC 1566 in the Dorado group to estimate its
IGM density.
