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 \texttt{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 a n=1 subring. We perform feature extraction with \texttt{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 \texttt{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).
Related
- Identification of Magnetic Reconnection in Plasma Turbulence Using a Dual-branch Lightweight Hybrid Convolutional Neural Network
- Stacked Hybrid Recurrent Convolutional Neural Network Reconstruction of X-Ray Influence on 21 cm Brightness Temperature
- Solar Flare Prediction Using a Hybrid Convolutional Neural Network and Transformer Model
- Neural Network Guided Parameter Space Constraints for Gravitational Wave Searches from Binary Black Holes
- Low-cost neural summaries for cosmological inference: an ensemble of convolutional neural networks trained on approximate simulations
BibTeX
@misc{duong2026convolutional,
title = {{Convolutional Neural Network for Extraction of $n = 1$ Photon Ring of Black Holes}},
author = {Courtney Duong and Frank Myhre and Joseph R. Farah},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25344},
eprint = {2609.25344},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25344},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Convolutional Neural Network for Extraction of n = 1 Photon Ring of Black Holes
AU - Courtney Duong
AU - Frank Myhre
AU - Joseph R. Farah
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.25344
UR - https://arxiv.org/abs/2609.25344
AB - 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 \texttt{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 a n=1 subring. We perform feature extraction with \texttt{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 \texttt{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).
ER -