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The Event Horizon Telescope recently observed the first shadow of a blackhole. Images like this can potentially be used to test or constrain theories ofgravity and deepen the understanding in plasma physics at event horizon scales,which requires accurate parameter estimations. In this work, we present DeepHorizon, two convolutional deep neural networks that recover the physicalparameters from images of black hole shadows. We investigate the effects of alimited telescope resolution and observations at higher frequencies. We trainedtwo convolutional deep neural networks on a large image library of simulatedmock data. The first network is a Bayesian deep neural regression network andis used to recover the viewing angle $i$, and position angle, mass accretionrate $\dot{M}$, electron heating prescription $R_{\rm high}$ and the black holemass $M_{\rm BH}$. The second network is a classification network that recoversthe black hole spin $a$. We find that with the current resolution of the EventHorizon Telescope, it is only possible to accurately recover a limited numberof parameters of a static image, namely the mass and mass accretion rate. Sincepotential future space-based observing missions will operate at frequenciesabove 230 GHz, we also investigated the applicability of our network at afrequency of 690 GHz. The expected resolution of space-based missions is higherthan the current resolution of the Event Horizon Telescope, and we show thatDeep Horizon can accurately recover the parameters of simulated observationswith a comparable resolution to such missions.

Deep Horizon; a machine learning network that recovers accreting black hole parameters