Convolutional Neural Network for Extraction of n = 1 Photon Ring of Black Holes

Abstract Very-long-baseline interferometry (VLBI) may enable direct imaging of fine, event-horizon-scale black hole structures such as the photon ring. In particular, the n = 1 photon subring encodes information about black hole spin in its radial profile and azimuthal brightness modulation, making it a key proxy for spacetime properties. However, the n = 1 subring overlaps with emission from the n = 0 subring in observations, requiring isolation of the n = 1 subring for measurements. We present a convolutional neural network (CNN) capable of extracting the n = 1 subring from composite images containing n = 0 and n = 1 subrings. We simulate 111,000 m -ring images with eht-imaging to train and validate the CNN. The CNN accurately recovers the radial and angular brightness profiles of the n = 1 subring with a mean normalized cross correlation (NXcorr) of ∼0.99. The CNN provides a detection capability by predicting no false positives when input images lack an n = 1 subring. We perform feature extraction with ringfit to show that the CNN-predicted n = 1 subring accurately recovers the ground truth radial profiles and azimuthal brightness modulation, demonstrating the CNN’s capability to extract spin-sensitive features. We further test the CNN on black hole images generated by KerrBAM and general relativistic magnetohydrodynamic (GRMHD) simulations, finding that it recovers the overall n = 1 subring radial profiles while exhibiting discrepancies in the recovered intensity profiles. These results demonstrate the potential of deep learning methods to isolate the n = 1 subring from overlapping emission, providing a framework for analyzing future high-resolution black hole images from proposed VLBI missions such as the Black Hole Explorer (BHEX).