Establishing Trust in Algorithmic Results: Ground Truth Simulations and the First Empirical Images of a Black Hole

When the first empirical images of a black hole’s shadow were released in April 2019, they transformed this defining black hole feature from a theoretical into an explorable physical entity. But although derived from empirical measurements, the production of these images relied on the deployment of the algorithmic pipelines designed specifically for this purpose to enable the selection of optimal imaging parameters. How could the researchers involved trust their imaging pipelines to deliver faithful reconstructions of unknown images from the noisy, fragmentary measurements? This article analyses the media-specific operations through which the researchers generated sufficient evidence of the trustworthiness of their algorithmic outputs by constructing and deploying a specifically tailored ground truth dataset that comprised simulated model images and simulated measurement data. The article also argues that the epistemic trust established through such operational procedures is relational and contingent on the adequacy of the media-specific operations that generated it.