  We present a method for estimating the velocity of a wandering black hole and
the equation of state for the gas around, based on a catalog of numerical
simulations. The method uses machine learning methods based on convolutional
neural networks applied to the classification of images resulting from
numerical simulations. Specifically we focus on the supersonic velocity regime
and choose the direction of the black hole to be parallel to its spin. We build
a catalog of 900 simulations by numerically solving Euler's equations onto the
fixed space-time background of a black hole, for two parameters: the adiabatic
index $\Gamma$ with values in the range [1.1, 5/3], and the asymptotic relative
velocity of the black hole with respect to the surroundings $v_{\infty}$, with
values within $[0.2, 0.8]c$. For each simulation we produce a 2D image of the
gas density once the process of accretion has approached a stationary regime.
The results obtained show that the implemented Convolutional Neural Networks
are capable to classify correctly the adiabatic index $87.78\%$ of the time
within an uncertainty of $\pm 0.0284$ while the prediction of the velocity is
correct $96.67\%$ of the times within an uncertainty of $\pm 0.03c$. We expect
that this combination of a massive number of numerical simulations and machine
learning methods will help analyze more complicated scenarios related to future
high resolution observations of black holes, like those from the Event Horizon
Telescope.
