Monte-Carlo Imaging for Optical Interferometry

TL;DR

This paper introduces a Monte-Carlo imaging algorithm for optical interferometry that employs simulated annealing and statistical analysis to improve image reconstruction and detect features like binary companions.

Contribution

The paper presents a novel Monte-Carlo based imaging code that reduces local minima issues and provides statistical limits on image features in optical interferometry.

Findings

Effective in avoiding local minima during image reconstruction.

Capable of placing statistical limits on unseen binary companions.

Demonstrated success on simulated data with various regularization schemes.

Abstract

We present a flexible code created for imaging from the bispectrum and visibility-squared. By using a simulated annealing method, we limit the probability of converging to local chi-squared minima as can occur when traditional imaging methods are used on data sets with limited phase information. We present the results of our code used on a simulated data set utilizing a number of regularization schemes including maximum entropy. Using the statistical properties from Monte-Carlo Markov chains of images, we show how this code can place statistical limits on image features such as unseen binary companions.