2022A&A...666A...9G


Query : 2022A&A...666A...9G

2022A&A...666A...9G - Astronomy and Astrophysics, volume 666A, 9 (2022/10-1)

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

GEBHARD T.D., BONSE M.J., QUANZ S.P. and SCHOLKOPF B.

Abstract (from CDS):


Context. 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.
Aims. 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.
Methods. 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 adapted 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 (nonlinear) 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.
Results. When we applied our method to four data sets from the VLT/NACO instrument, our algorithm provided a better false-positive fraction than a popular baseline method in the field. Additionally, we found 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 a first piece of evidence that using the observing conditions as additional predictors can improve the results.
Conclusions. 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.

Abstract Copyright: © T. D. Gebhard et al. 2022

Journal keyword(s): methods: data analysis - techniques: image processing - planets and satellites: detection

Simbad objects: 3

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Number of rows : 3
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2024
#notes
1 * bet Pic PM* 05 47 17.0876901 -51 03 59.441135 4.13 4.03 3.86 3.74 3.58 A6V 1908 1
2 V* R CrA Ae* 19 01 53.6764322232 -36 57 08.299341828 12.781 12.651 11.917 11.242 10.412 B5IIIpe 478 1
3 HD 218396 El* 23 07 28.7157209544 +21 08 03.310767492   6.21 5.953     F0+VkA5mA5 1141 0

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