Monte-Carlo Imaging for Optical Interferometry
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.
Cite
@article{arxiv.2007.00716,
title = {Monte-Carlo Imaging for Optical Interferometry},
author = {Michael J. Ireland and John D. Monnier and Nathalie Thureau},
journal= {arXiv preprint arXiv:2007.00716},
year = {2020}
}
Comments
Preprint version of paper published in 2006 SPIE proceedings. Please contact John Monnier (monnier@umich.edu) for current distribution of the MACIM software