A High-contrast Imaging Algorithm: Optimized Image Rotation and Subtraction

TL;DR

This paper introduces an optimized version of the Image Rotation and Subtraction (IRS) technique, called OIRS, which enhances high-contrast imaging performance, especially at small angular separations, by reducing speckle noise without planet self-subtraction.

Contribution

The paper presents an optimization algorithm for IRS that improves contrast at small angular separations and demonstrates its effectiveness compared to existing methods.

Findings

OIRS provides higher S/N at small angular separations.

OIRS outperforms LOCI in contrast enhancement.

The method effectively reduces speckle noise without planet self-subtraction.

Abstract

Image Rotation and Subtraction (IRS) is a high-contrast imaging technique which can be used to suppress the speckles noise and facilitate the direct detection of exoplanets. IRS is different from Angular Differential Imaging (ADI), in which it will subtract a copy of the image with 180 degrees rotated around its PSF center, rather than the subtraction of the median of all of the PSF images. Since the planet itself will be rotated to the other side of the PSF, IRS does not suffer from planet self-subtraction. In this paper, we have introduced an optimization algorithm to IRS (OIRS), which can provide an extra contrast gain at small angular separations. The performance of OIRS has been demonstrated with ADI data. We then made a comparison of the signal to noise ratio (S/N) achieved by algorithms of locally optimized combination of images (LOCI) and OIRS. Finally we found that OIRS algorithm can deliver a better S/N for small angular separations.