arXiv subjects

I. Natarajan

Publications and source records attributed to I. Natarajan.

2 recordsLinked to original sources

arXiv2025

Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library

(abridged) In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of Sgr A* and M87*. Here, 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 GRMHD 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. 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. 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. 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.

astro-ph.IM↗

arXiv2020

SYMBA: An end-to-end VLBI synthetic data generation pipeline

Realistic synthetic observations of theoretical source models are essential for our understanding of real observational data. In using synthetic data, one can verify the extent to which source parameters can be recovered and evaluate how various data corruption effects can be calibrated. These studies are important when proposing observations of new sources, in the characterization of the capabilities of new or upgraded instruments, and when verifying model-based theoretical predictions in a comparison with observational data. We present the SYnthetic Measurement creator for long Baseline Arrays (SYMBA), a novel synthetic data generation pipeline for Very Long Baseline Interferometry (VLBI) observations. SYMBA takes into account several realistic atmospheric, instrumental, and calibration effects. We used SYMBA to create synthetic observations for the Event Horizon Telescope (EHT), a mm VLBI array, which has recently captured the first image of a black hole shadow. After testing SYMBA with simple source and corruption models, we study the importance of including all corruption and calibration effects. Based on two example general relativistic magnetohydrodynamics (GRMHD) model images of M87, we performed case studies to assess the attainable image quality with the current and future EHT array for different weather conditions. The results show that the effects of atmospheric and instrumental corruptions on the measured visibilities are significant. Despite these effects, we demonstrate how the overall structure of the input models can be recovered robustly after performing calibration steps. With the planned addition of new stations to the EHT array, images could be reconstructed with higher angular resolution and dynamic range. In our case study, these improvements allowed for a distinction between a thermal and a non-thermal GRMHD model based on salient features in reconstructed images.

astro-ph.IM↗