Identifying black holes through space telescopes and deep learning

The Event Horizon Telescope (EHT) has captured a series of images of black holes. These images could provide valuable information about the gravitational environment near the event horizon. However, accurate detection and parameter estimation for candidate black holes are necessary. This paper explores the potential for identifying black holes in the ultraviolet band using space telescopes. We establish a data pipeline for generating simulated observations and present an ensemble neural network model for black hole detection and parameter estimation. For detection tasks, the model achieves mean average precision [0.5] values of 0.9176 even when reaching the imaging full width half maximum (FWHM) (${\ensuremath{\theta}}_{c}$) and maintains the detection ability until $0.54{\ensuremath{\theta}}_{c}$. For parameter estimation tasks, the model can accurately recover the inclination, position angle, accretion disk temperature and black hole mass. These results indicate that our methodology can go beyond the limits of the traditional Rayleigh diffraction limit and enable superresolution recognition. Moreover, the model successfully detects the shadow of M87* from background noise and other celestial bodies, and estimates its inclination and position angle. Our work demonstrates the feasibility of detecting black holes in the ultraviolet band and provides a new method for black hole detection and further parameter estimation.