Topological Data Analysis of Black Hole Images

Abstract

Features such as photon rings, jets, or hot. spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images into a form that is suitable for topological analysis.

PRD accepted -- matches accepted version

References in corpus (8)

• First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole
• First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole
• First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole
• First M87 Event Horizon Telescope Results. V. Physical Origin of the Asymmetric Ring
• First M87 Event Horizon Telescope Results. II. Array and Instrumentation
• First M87 Event Horizon Telescope Results. III. Data Processing and Calibration
• Observing the Inner Shadow of a Black Hole: A Direct View of the Event Horizon
• Super-resolution Full Polarimetric Imaging for Radio Interferometry with Sparse Modeling

Cited by in corpus (2)

• Shadow Geometry of Kerr Naked Singularities
• Identifying highly magnetized white dwarfs: A dimensionality reduction framework for estimating magnetic fields