Autoencoding Labeled Interpolator, Inferring Parameters From Image, And
  Image From Parameters
Ali SaraerToosi Avery Broderick
The Event Horizon Telescope (EHT) provides an avenue to study black hole
accretion flows on event-horizon scales. Fitting a semi-analytical 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 both interpolating 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 system.