Astronomy and Astrophysics, volume 621A, 59-59 (2019/1-1)
PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data.
STOLKER T., BONSE M.J., QUANZ S.P., AMARA A., CUGNO G., BOHN A.J. and BOEHLE A.
Abstract (from CDS):
Context. The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and point spread function (PSF)-subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets.
Aims. We aim at developing a generic and modular data-reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and particularly suitable for the 3-5µm wavelength range where typically thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical.
Methods. PynPoint is written in Python 2.7 and applies various image-processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF-subtraction tool based on principal component analysis (PCA).
Results. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L' and M' data of β Pic b and reassessed the brightness and position of the planet with a Markov chain Monte Carlo analysis; we also provide a derivation of the photometric error budget.
© ESO 2019
methods: data analysis - techniques: high angular resolution - techniques: image processing - planets and satellites: detection
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