Inference in optical interferometry: a quick review of modeling and imaging techniques

Fabien R. Baron

doi:10.1117/12.2561582

13 December 2020 Inference in optical interferometry: a quick review of modeling and imaging techniques

Fabien R. Baron

Proceedings Volume 11446, Optical and Infrared Interferometry and Imaging VII; 114461N (2020) https://doi.org/10.1117/12.2561582
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Abstract

The problem of inference in optical interferometry, i.e. turning the on-sky data into meaningful astrophysics, is a difficult ill-posed problem. But in the last two decades, several exciting developments have taken place and novel algorithms have arisen; in imaging: multi-wavelength imaging, dynamical imaging, imaging on spheroids, and production of error bars on images; in model-fitting: new bootstrapping techniques and Bayesian model selection for model-fitting. Both the characterization of the data (likelihood) and of our expectation of the solution (regularization) have improved. Buzzword-sounding techniques such as Compressed Sensing, Machine Learning, ADMM, and GPU Computing are now finding practical applications. The recent algorithmic work by the Event Horizon Telescope team has also sparked interest in optical interferometry. This paper covers these topics in an attempt to predict what the future holds for inference in our field.

Conference Presentation

Citation Download Citation

Fabien R. Baron "Inference in optical interferometry: a quick review of modeling and imaging techniques", Proc. SPIE 11446, Optical and Infrared Interferometry and Imaging VII, 114461N (13 December 2020); https://doi.org/10.1117/12.2561582

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