New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline

TL;DR

ACORNS-ADI is a new, efficient, open-source software pipeline that introduces novel algorithms for high-contrast imaging data reduction, improving sensitivity and noise reduction in exoplanet imaging surveys.

Contribution

The paper presents new algorithms for calibration, registration, and sensitivity analysis in high-contrast imaging, implemented in a parallelized Python package for the first time.

Findings

Noise reduction up to ~20% using trimmed mean

Robust sensitivity computation without artificial sources

Effective removal of detector electronic artifacts

Abstract

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.