A Red-Noise Eigenbasis for the Reconstruction of Blobby Images

TL;DR

This paper introduces a PCA-derived eigenbasis from red-noise images that effectively reconstructs diverse astronomical source images, showing robustness across different spectral parameters.

Contribution

The paper presents a novel eigenbasis based on red-noise PCA that improves image reconstruction for astronomical sources, regardless of spectral parameter variations.

Findings

Eigenbasis enables efficient reconstruction of diverse images.

Reconstruction quality is weakly dependent on spectral parameters.

Method is applicable to interferometric astronomical imaging.

Abstract

We demonstrate the use of an eigenbasis that is derived from principal component analysis (PCA) applied on an ensemble of random-noise images that have a "red" power spectrum; i.e., a spectrum that decreases smoothly from large to small spatial scales. The pattern of the resulting eigenbasis allows for the reconstruction of images with a broad range of image morphologies. In particular, we show that this general eigen basis can be used to efficiently reconstruct images that resemble possible astronomical sources for interferometric observations; even though the images in the original ensemble used to generate the PCA basis are significantly different from the astronomical images. We further show that the efficiency and fidelity of the image reconstructions depends only weakly on the particular parameters of the red-noise power spectrum used to generate the ensemble of images.