Abstract
During the last two decades, the first generation of beam combiners at the Very Large Telescope Interferometer has proved the importance of optical interferometry for high-angular resolution astrophysical studies in the near- and mid-infrared. With the advent of 4-beam combiners at the VLTI, the u − v coverage per pointing increases significantly, providing an opportunity to use reconstructed images as powerful scientific tools. Therefore, interferometric imaging is already a key feature of the new generation of VLTI instruments, as well as for other interferometric facilities like CHARA and JWST. It is thus imperative to account for the current image reconstruction capabilities and their expected evolutions in the coming years. Here, we present a general overview of the current situation of optical interferometric image reconstruction with a focus on new wavelength-dependent information, highlighting its main advantages and limitations. As an Appendix we include several cookbooks describing the usage and installation of several state-of-the art image reconstruction packages. To illustrate the current capabilities of the software available to the community, we recovered chromatic images, from simulated MATISSE data, using the MCMC software SQUEEZE. With these images, we aim at showing the importance of selecting good regularization functions and their impact on the reconstruction.
Highlights
Except perhaps for the most simple objects, interferometric data are hard to interpret directly, and image reconstruction is a powerful tool for scientific analysis of the observations
Since direct inversion of the data is neither possible nor recommended, image reconstruction algorithms are based on regularized minimization processes that iteratively solve an ill-posed problem, where the model of the data given the image is compared to the actual data, in order to determine how to better fit the data while respecting some imposed constraints
These constraints are needed to avoid over-fitting of the data and to compensate for the sparsity of the data which leads to an under-determined problem
Summary
Except perhaps for the most simple objects, interferometric data are hard to interpret directly, and image reconstruction is a powerful tool for scientific analysis of the observations. The great advantage of this method is that it could find a global minimum, at the cost of being significantly slower than Gradient Descent By using these methods, several image reconstruction software have been developed to analyse optical/infrared interferometric data, and most of them are available to the community. During the last twenty years several algorithms have reached sufficient maturity to produce science-grade images, among them we have: the Building-Block Method [12], BSMEM [5], MiRA [44], SQUEEZE [3], WISARD [19], and IRBis [13] The capabilities of these software to recover images from optical/infrared long-baseline interferometric data have been shown several times in the community through the “Interferometric Imaging Beauty Contest” (see e.g., [36]). At the VIII edition of the “Interferometric Imaging Beauty Contest” a benchmark of different algorithms was done with simulated chromatic data for the first time [34]
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