Image reconstruction in optical interferometry: benchmarking the regularization

Context. With the advent of visible and infrared long-baseline interferometers with more than two telescopes, both the size and the completeness of interferometric data sets have significantly increased, allowing images based on models with no a priori assumptions to be reconstructed with an aperture synthesis technique. Aims. Our main objective is to analyze the multiple parameters of the image reconstruction process with particular attention to the regularization term and the study of their behavior in different situations (types of astrophysical objects, telescope array configurations, level of noise, etc.). The secondary goal is to derive practical rules for the users. Methods. Using the Multi-aperture image Reconstruction Algorithm (MiRA), we performed multiple systematic tests, analyzing 11 regularization terms commonly used. The tests are made on different astrophysical objects, different (u, v) plane coverages and several signal-to-noise ratios to determine the minimal configuration needed to reconstruct an image. We establish a methodology and we introduce the mean-square errors (MSE) to discuss the results. Results. From the similar to 24 000 simulations performed for the benchmarking of image reconstruction with MiRA, we are able to classify the different regularizations in the context of the observations. We find typical values of the regularization weight. A minimal (u, v) coverage is required to reconstruct an acceptable image, whereas no limits are found for the studied values of the signal-to-noise ratio. We also show that super-resolution can be achieved with increasing performance with the (u, v) coverage filling. Conclusions. Using image reconstruction with a sufficient (u, v) coverage is shown to be reliable. The choice of the main parameters of the reconstruction is tightly constrained. We recommend that efforts to develop interferometric infrastructures should first concentrate on the number of telescopes to combine, and secondly on improving the accuracy and sensitivity of the arrays.