Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters

Abstract

The Event Horizon Telescope (EHT) provides an avenue to study black hole accretion flows on event-horizon scales. Fitting a semianalytical model to EHT observations requires the construction of synthetic images, which is computationally expensive. This study presents an image generation tool in the form of a generative machine-learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. Trained on a set of synthetic black hole images, our tool showcases success in interpolating both black hole images and their associated physical parameters. By reducing the computational cost of generating an image, this tool facilitates parameter estimation and model validation for observations of black hole systems.

Keywords

• Neural networks
• Convolutional neural networks
• GPU computing
• Black hole physics
• High energy astrophysics
• Accretion