Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework
A&A 666, A9 (2022) High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Existing post-processing algorithms do not use all prior domain knowle...
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Main Authors: | , , , |
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Format: | Journal Article |
Language: | English |
Published: |
07-04-2022
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Subjects: | |
Online Access: | Get full text |
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Summary: | A&A 666, A9 (2022) High-contrast imaging of exoplanets hinges on powerful post-processing
methods to denoise the data and separate the signal of a companion from its
host star, which is typically orders of magnitude brighter. Existing
post-processing algorithms do not use all prior domain knowledge that is
available about the problem. We propose a new method that builds on our
understanding of the systematic noise and the causal structure of the
data-generating process. Our algorithm is based on a modified version of
half-sibling regression (HSR), a flexible denoising framework that combines
ideas from the fields of machine learning and causality. We adapt the method to
address the specific requirements of high-contrast exoplanet imaging data
obtained in pupil tracking mode. The key idea is to estimate the systematic
noise in a pixel by regressing the time series of this pixel onto a set of
causally independent, signal-free predictor pixels. We use regularized linear
models in this work; however, other (non-linear) models are also possible. In a
second step, we demonstrate how the HSR framework allows us to incorporate
observing conditions such as wind speed or air temperature as additional
predictors. When we apply our method to four data sets from the VLT/NACO
instrument, our algorithm provides a better false-positive fraction than
PCA-based PSF subtraction, a popular baseline method in the field.
Additionally, we find that the HSR-based method provides direct and accurate
estimates for the contrast of the exoplanets without the need to insert
artificial companions for calibration in the data sets. Finally, we present
first evidence that using the observing conditions as additional predictors can
improve the results. Our HSR-based method provides an alternative, flexible and
promising approach to the challenge of modeling and subtracting the stellar PSF
and systematic noise in exoplanet imaging data. |
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DOI: | 10.48550/arxiv.2204.03439 |