Deep learning inference with the Event Horizon Telescope

I. Calibration improvements and a comprehensive synthetic data library

¹ Department of Astrophysics, Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
² Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany
³ Steward Observatory and Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
⁴ Data Science Institute, University of Arizona, 1230 N. Cherry Ave., Tucson, AZ 85721, USA
⁵ Program in Applied Mathematics, University of Arizona, 617 N. Santa Rita Ave., Tucson, AZ 85721, USA
⁶ Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA
⁷ Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
⁸ Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA
⁹ Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8, Canada
¹⁰ David A. Dunlap Department of Astronomy, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada
¹¹ Department of Physics, University of Toronto, 60 St. George Street, Toronto, ON M5S 1A7, Canada
¹² Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada
¹³ University of Southern California – Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA
¹⁴ Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s/n, 18008 Granada, Spain
¹⁵ Departamento de Matemática da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications (CIDMA), Campus de Santiago, 3810-193 Aveiro, Portugal

^★ Corresponding author: M.Janssen@astro.ru.nl

Received: 16 January 2025
Accepted: 7 April 2025

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 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. Parameter inference is mostly limited by intrinsic model variability, which highlights the importance of long-term monitoring observations with the EHT. In later papers in this series, we will show how a Bayesian neural network trained on our synthetic data is capable of dealing with the model variability and extracting physical parameters from EHT observations. With our calibration improvements, our newly reduced EHT datasets have a considerably better quality compared to previously analyzed data.