Publications

Deep learning inference with the Event Horizon Telescope

Abstract

Context. In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of the observed Sagittarius A*(Sgr A∗) and Messier 87*(M87∗) horizon-scale sources. Aims. In this first article, we report on improvements made to our observational data reduction pipeline and present the generation of observables derived from the EHT models. We make use of ray-traced general relativistic magnetohydrodynamic simulations that are based on different black hole spacetime metrics and accretion physics parameters. These broad classes of models provide a good representation of the primary targets observed by the EHT.
Methods. We describe how we combined multiple frequency bands and polarization channels of the observational data to improve our fringe-finding sensitivity and stabilization of atmospheric phase fluctuations. To generate realistic synthetic data from our models, we took the signal path as well as the calibration process, and thereby the aforementioned improvements, into account. We could thus produce synthetic visibilities akin to calibrated EHT data and identify salient features for the discrimination of model parameters. Results. We have produced a library consisting of an unparalleled 962,000 synthetic Sgr A∗ and M87∗ datasets. In terms of baseline coverage and noise properties, the library encompasses 2017 EHT measurements as well as future observations with an extended telescope array.
Conclusions. We differentiate between robust visibility data products related to model features and data products that are strongly affected by data corruption effects …

Date
2025
Authors
J Röder, M Rynge, M Wielgus