More data than you want, less data than you need: machine learning approaches to starlight subtraction with MagAO-X

TL;DR

This paper explores machine learning techniques using MagAO-X sensor data to improve starlight subtraction in high-contrast imaging, aiming to enhance detection of exoplanets and disks at smaller separations.

Contribution

It introduces a novel PSF modeling approach that leverages all available sensor data and machine learning to reduce over-subtraction in high-contrast imaging.

Findings

Large volume of sensor data enables effective machine learning models.

The approach improves sensitivity for detecting smaller star-planet separations.

Potential to enhance rocky planet imaging capabilities.

Abstract

High-contrast imaging data analysis depends on removing residual starlight from the host star to reveal planets and disks. Most observers do this with principal components analysis (i.e. KLIP) using modes computed from the science images themselves. These modes may not be orthogonal to planet and disk signals, leading to over-subtraction. The wavefront sensor data recorded during the observation provide an independent signal with which to predict the instrument point-spread function (PSF). MagAO-X is an extreme adaptive optics (ExAO) system for the 6.5-meter Magellan Clay telescope and a technology pathfinder for ExAO with GMagAO-X on the upcoming Giant Magellan Telescope. MagAO-X is designed to save all sensor information, including kHz-speed wavefront measurements. Our software and compressed data formats were designed to record the millions of training samples required for machine learning with high throughput. The large volume of image and sensor data lets us learn a PSF model incorporating all the information available. This will eventually allow us to probe smaller star-planet separations at greater sensitivities, which will be needed for rocky planet imaging.