Analysis of LBT LINC-NIRVANA simulated images of galaxies and young stellar objects

Abstract

LINC-NIRVANA (LN) is a Fizeau interferometer that will provide for the first time coherent images in the near-IR combining the beams from the two Large Binocular Telescope (LBT)arms, by adopting a Multi-Coniugate Adaptive Optics system (MCAO) that allows for atmospheric turbulence compensation. We applied a software for the simulation and the reconstruction of LN images (AIRY-LN, see Desiderá et al.1 this Conference) in two specific scientific cases: a relatively distant galaxy at redshift about 1 and a collimated jet from a Young Stellar Object (YSO). These two cases have been chosen to test the capability of LN in the observations of faint and small (1-2 arcsec) extragalactic objects and objects with diffuse emission and high dynamical range, respectively. A total of six images at different hour angles have been obtained for both cases. Using these simulated images, we obtained the final reconstructed images using the software package AIRY-LN. These images have been analyzed with the standard data reduction software (IRAF and IDL). Our analysis show that the reconstruction algorithm is fundamental to obtain a good reproduction of the original flux and morphology while the optimal number of iterations strongly depends on the scientific goal.

Reprint of Inexact Bregman iteration for deconvolution of superimposed extended and point sources

“…where x b is the solution of (8). For all test-problems, bisection iterations show that at the extremes of the interval ½10 À10 ; 10 À8 , the discrepancy function H g ðbÞ changes the sign.…”

“…Since f b ð xÞ is nonnegative, coercive and convex, there exists a solution for problem (8). Under suitable assumptions, this solution is unique.…”

“…In order to obtain the numerical solution of the minimization problem (8), there exists a number of algorithms specifically tailored for the KL divergence combined with a differentiable regularization term (for a survey see [1]). In the class of first order methods requiring only function and gradient evaluations, the Scaled Gradient Projection (SGP) method is very efficient for image restoration from Poisson data [3,20].…”

“…The meaning of the parameters appearing in the table is defined within the text. Table 3 Numerical results obtained by applying SGP method to problem (8);b corresponds to the best relative reconstruction error q for the diffuse component obtained by several trials with different values of b. The meaning of the parameters appearing in the table is defined within the text.…”

“…This is the case, for instance, of the study of circumstellar structures around Young Stellar Objects (YSO). Previous studies on YSO simulations [8] shows that the reconstruction of the diffuse component by standard deconhttp://dx.doi.org/10.1016/j.cnsns.2014. 10.020 1007-5704/Ó 2014 Elsevier B.V. All rights reserved.…”