BHCast: Unlocking Black Hole Plasma Dynamics from a Single Blurry Image with Long-Term Forecasting

Abstract

The Event Horizon Telescope (EHT) delivered the first image of a black hole by capturing the light from its surrounding accretion flow, revealing structure but not dynamics. Simulations of black hole accretion dynamics are essential for interpreting EHT images but costly to generate and impractical for inference. Motivated by this bottleneck, BHCast presents a framework for forecasting black hole plasma dynamics from a single, blurry snapshot, such as those captured by the EHT. At its core, BHCast is a neural model that transforms a static image into forecasted future frames, revealing the underlying dynamics hidden within one snapshot. With a multi-scale pyramid loss, we demonstrate how autoregressive forecasting can simultaneously super-resolve and evolve a blurry frame into a coherent, high-resolution movie that remains stable over long time horizons. From forecasted dynamics, we can then extract interpretable spatio-temporal features, such as pattern speed (rotation rate) and pitch angle. Finally, BHCast uses gradient-boosting trees to recover black hole properties from these plasma features, including the spin and viewing inclination angle. The separation between forecasting and inference provides modular flexibility, interpretability, and robust uncertainty quantification. We demonstrate the effectiveness of BHCast on simulations of two distinct black hole accretion systems, Sagittarius A* and M87*, by testing on simulated frames blurred to EHT resolution and real EHT images of M87*. Ultimately, our methodology establishes a scalable paradigm for solving inverse problems, demonstrating the potential of learned dynamics to unlock insights from resolution-limited scientific data.

Figures (15)

• Figure 1: From a Single Blurry Frame to Black-Hole Dynamics. A single blurry image, reconstructed from Event Horizon Telescope measurements, is used to forecast a dynamic sequence of high-resolution frames. From this forecasted movie, features of accretion disk plasma can be extracted to infer the black hole's physical properties. This sequential framework—forecasting, feature extraction, and parameter estimation—is robust, versatile, and fully interpretable, providing a new pathway for analyzing horizon-scale dynamics.
• Figure 2: Training and inference pipelines of BHCast. We train the dynamics forecast model (U-Net) to predict the next frame and the physics inference model (XGBoost) to estimate black hole parameters. During inference, we autoregressively forecast with the U-Net to produce cylinder plots to extract plasma features, which are inputs to XGBoost for physics inference.
• Figure 3: Loss ablation study. LPIPS across three losses: $\ell_2$ only, multi-scale without mean-flux, and the full BHCast loss. $\ell_2$-only performs poorly at all times, while the multi-scale variant works briefly but becomes unstable. The full loss, combining multi-scale and mean-flux terms, yields stable long-term forecasts.
• Figure 4: Super-Resolution Evaluation: Power Spectral Density (PSD) and log-scale visualizations of the input, prediction after 6 steps (30 $\,GMc^{-3}$ ), and prediction after 100 steps (500 $\,GMc^{-3}$ ) for (A) face-on GRMHD and (B) edge-on GRMHD. At input, BHCast lacks high-frequency details but recovers them by step 6 to match the ground truth PSD. Its forecasts also visually converge to the ground truth. The oracle optical flow baseline starts with the deconvolution result but shows limited super-resolution. The learning-based baseline is omitted due to divergence after a few steps.
• Figure 5: Forecasting Fidelity Evaluation. Perceptual similarity (LPIPS; lower is better) between forecasts and the unblurred ground-truth simulation. BHCast's forecast remains visually close to the ground truth over long horizons, while baseline forecasts are on par with the EHT-resolution blurred input.