A PyTorch Benchmark for High-Contrast Imaging Post Processing

TL;DR

This paper introduces torchKLIP, a PyTorch-based benchmark package that leverages machine learning techniques to improve the post-processing of high-contrast astronomical images by better subtracting stellar speckle noise.

Contribution

The work presents a novel PyTorch benchmark for high-contrast imaging post-processing, enabling ML methods to utilize PSF libraries for enhanced exoplanet detection.

Findings

torchKLIP facilitates ML-based speckle noise subtraction.

Improved detection capabilities for faint exoplanets.

Potential integration with advanced telescopic instruments.

Abstract

Direct imaging of exoplanets is a challenging task that involves distinguishing faint planetary signals from the overpowering glare of their host stars, often obscured by time-varying stellar noise known as "speckles". The predominant algorithms for speckle noise subtraction employ principal-based point spread function (PSF) fitting techniques to discern planetary signals from stellar speckle noise. We introduce torchKLIP, a benchmark package developed within the machine learning (ML) framework PyTorch. This work enables ML techniques to utilize extensive PSF libraries to enhance direct imaging post-processing. Such advancements promise to improve the post-processing of high-contrast images from leading-edge astronomical instruments like the James Webb Space Telescope and extreme adaptive optics systems.