Towards independent event horizon imaging of the supermassive black holes in M87 and the Milky Way
Abstract
The Event Horizon Telescope (EHT) Collaboration's images of the supermassive black holes in M87 and the Milky Way have provided the first event-horizon-scale views of these objects, opening new avenues for studies of gravitation, accretion physics, and black hole astrophysics. Achieving these results, however, requires imaging under some of the most challenging conditions in radio astronomy, including low signal-to-noise ratios, severe calibration uncertainties, and sparse aperture coverage. With the aim of presenting independent analyses of the public EHT datasets for M87* and Sgr A*, we adopt an approach that is independent in observables, and reconstruction methodology. Our framework is based on closure invariants, a class of interferometric observables that are intrinsically immune to station-based calibration errors and therefore provide robust constraints on source structure. We combine these observables with Generative Deep learning Image Reconstruction with Closure Terms (GenDIReCT), a diffusion-based image reconstruction framework that operates in the latent space of images conditioned on closure invariants. We present independent reconstructions obtained using GenDIReCT on synthetic challenge data sets as well as real EHT data on 3C279 and Centaurus A, and compare them with previously reported results. This work demonstrates the potential of closure-invariant-driven generative imaging as a calibration-resilient framework for Very Long Baseline Interferometry (VLBI) and provides an independent and complementary avenue for interpreting horizon-scale black hole observations.
Versions
Related
- Numerical Simulations of the interaction between winds from tidal disruption event and supermassive black hole circumnuclear medium
- Observational evidence of black hole event horizon from ultra-compact X-ray binaries
- The First Model-independent Upper Bound on Microlensing Signature of the Highest-mass Binary Black Hole Event GW231123
- Chaotic accretion can (still) explain early supermassive black hole growth
- Parameter Estimation for Eccentric Supermassive Black Hole Binaries with Pulsar Timing Arrays
BibTeX
@misc{thyagarajan2026towards,
title = {{Towards independent event horizon imaging of the supermassive black holes in M87 and the Milky Way}},
author = {Nithyanandan Thyagarajan and Samuel Lai and Ivy Wong and Foivos Diakogiannis},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.19675},
eprint = {2608.19675},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.19675},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Towards independent event horizon imaging of the supermassive black holes in M87 and the Milky Way
AU - Nithyanandan Thyagarajan
AU - Samuel Lai
AU - Ivy Wong
AU - Foivos Diakogiannis
PY - 2026
DA - 2026/08/20
PB - arXiv
DO - 10.48550/arxiv.2608.19675
UR - https://arxiv.org/abs/2608.19675
AB - The Event Horizon Telescope (EHT) Collaboration's images of the supermassive black holes in M87 and the Milky Way have provided the first event-horizon-scale views of these objects, opening new avenues for studies of gravitation, accretion physics, and black hole astrophysics. Achieving these results, however, requires imaging under some of the most challenging conditions in radio astronomy, including low signal-to-noise ratios, severe calibration uncertainties, and sparse aperture coverage. With the aim of presenting independent analyses of the public EHT datasets for M87* and Sgr A*, we adopt an approach that is independent in observables, and reconstruction methodology. Our framework is based on closure invariants, a class of interferometric observables that are intrinsically immune to station-based calibration errors and therefore provide robust constraints on source structure. We combine these observables with Generative Deep learning Image Reconstruction with Closure Terms (GenDIReCT), a diffusion-based image reconstruction framework that operates in the latent space of images conditioned on closure invariants. We present independent reconstructions obtained using GenDIReCT on synthetic challenge data sets as well as real EHT data on 3C279 and Centaurus A, and compare them with previously reported results. This work demonstrates the potential of closure-invariant-driven generative imaging as a calibration-resilient framework for Very Long Baseline Interferometry (VLBI) and provides an independent and complementary avenue for interpreting horizon-scale black hole observations.
ER -