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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 ~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
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