Data Reduction and Image Reconstruction Techniques for Non-Redundant Masking

TL;DR

This paper presents a comprehensive data reduction pipeline for non-redundant masking (NRM), including calibration, Fourier analysis, and image reconstruction techniques, with applications to current and future large telescopes.

Contribution

It introduces a detailed NRM data processing pipeline, compares imaging algorithms, and discusses implications for next-generation extremely large telescopes.

Findings

Calibration strategies reduce Fourier measurement scatter.

Different image reconstruction methods impact image quality.

Next-generation telescopes will benefit from improved NRM techniques.

Abstract

The technique of non-redundant masking (NRM) transforms a conventional telescope into an interferometric array. In practice, this provides a much better constrained point spread function than a filled aperture and thus higher resolution than traditional imaging methods. Here we describe an NRM data reduction pipeline. We discuss strategies for NRM observations regarding dithering patterns and calibrator selection. We describe relevant image calibrations and use example Large Binocular Telescope datasets to show their effects on the scatter in the Fourier measurements. We also describe the various ways to calculate Fourier quantities, and discuss different calibration strategies. We present the results of image reconstructions from simulated observations where we adjust prior images, weighting schemes, and error bar estimation. We compare two imaging algorithms and discuss implications for reconstructing images from real observations. Finally, we explore how the current state of the art compares to next generation Extremely Large Telescopes.