Unlocking starlight subtraction in full data rate exoplanet imaging by efficiently updating Karhunen-Lo\`eve eigenimages

TL;DR

This paper introduces an efficient method to update Karhunen-Loève eigenimages for starlight subtraction in exoplanet imaging, enabling faster analysis of large datasets without full recomputation.

Contribution

The authors develop a technique to downdate the SVD of the dataset, significantly speeding up the KLIP algorithm for large data volumes in exoplanet imaging.

Findings

Achieved speedups of 2.6x to 140x depending on dataset size.

Enabled analysis of larger datasets in same computational time.

Demonstrated near-linear scaling in runtime with number of observations.

Abstract

Starlight subtraction algorithms based on the method of Karhunen-Lo\`eve eigenimages have proved invaluable to exoplanet direct imaging. However, they scale poorly in runtime when paired with differential imaging techniques. In such observations, reference frames and frames to be starlight-subtracted are drawn from the same set of data, requiring a new subset of references (and eigenimages) for each frame processed to avoid self-subtraction of the signal of interest. The data rates of extreme adaptive optics instruments are such that the only way to make this computationally feasible has been to downsample the data. We develop a technique that updates a pre-computed singular value decomposition of the full dataset to remove frames (i.e. a "downdate") without a full recomputation, yielding the modified eigenimages. This not only enables analysis of much larger data volumes in the same amount of time, but also exhibits near-linear scaling in runtime as the number of observations increases. We apply this technique to archival data and investigate its scaling behavior for very large numbers of frames $N$. The resulting algorithm provides speed improvements of $2.6\times$ (for 200 eigenimages at $N = 300$) to $140 \times$ (at $N = 10^4$) with the advantage only increasing as $N$ grows. This algorithm has allowed us to substantially accelerate KLIP even for modest $N$, and will let us quickly explore how KLIP parameters affect exoplanet characterization in large $N$ datasets.