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.

Preprint version of paper published in 2006 SPIE proceedings. Please contact John Monnier ([email protected]) for current distribution of the MACIM software

References in corpus (2)

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