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High-contrast imaging of exoplanets hinges on powerful post-processingmethods to denoise the data and separate the signal of a companion from itshost star, which is typically orders of magnitude brighter. Existingpost-processing algorithms do not use all prior domain knowledge that isavailable about the problem. We propose a new method that builds on ourunderstanding of the systematic noise and the causal structure of thedata-generating process. Our algorithm is based on a modified version ofhalf-sibling regression (HSR), a flexible denoising framework that combinesideas from the fields of machine learning and causality. We adapt the method toaddress the specific requirements of high-contrast exoplanet imaging dataobtained in pupil tracking mode. The key idea is to estimate the systematicnoise in a pixel by regressing the time series of this pixel onto a set ofcausally independent, signal-free predictor pixels. We use regularized linearmodels in this work; however, other (non-linear) models are also possible. In asecond step, we demonstrate how the HSR framework allows us to incorporateobserving conditions such as wind speed or air temperature as additionalpredictors. When we apply our method to four data sets from the VLT/NACOinstrument, our algorithm provides a better false-positive fraction thanPCA-based PSF subtraction, a popular baseline method in the field.Additionally, we find that the HSR-based method provides direct and accurateestimates for the contrast of the exoplanets without the need to insertartificial companions for calibration in the data sets. Finally, we presentfirst evidence that using the observing conditions as additional predictors canimprove the results. Our HSR-based method provides an alternative, flexible andpromising approach to the challenge of modeling and subtracting the stellar PSFand systematic noise in exoplanet imaging data.

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework