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

Characterizing the velocity of a wandering black hole and properties of the surrounding medium using convolutional neural networks