Direct Imaging and wavefront control- Data Reduction Flashcards
Describe various post-processing methods for improving point-spread function subtraction.
ADI: A sequence of images is acquired with an altitude/azimuth telescope while the instrument field derotator is switched off. This keeps the instrument and telescope optics aligned and allows the field of view to rotate with respect to the instrument. For each image, a reference PSF is constructed from other appropriately-selected images of the same sequence and subtracted to remove the quasi-static PSF structure. All residual images are then rotated to align the field and are combined.
SDI: The reference frames are the images of the target star across different wavelengths, and they are linear combined and can subtract the speckle noise from stellar PSF. Removes edge on structures, unlike ADI which can remove radial structures
RDI: The reference frames may be specific reference stars purposely taken on the same night as the primary observation, frames from other stars that were coincidentally observed on the same night in the same observing mode, or from an archive of frames across observing programs. An advantage is that RDI eliminates the possibility of self-subtraction.
PDI: Polarimetric differential image– Takes advantage of the fact that the direct stellar light is essentially unpolarized whereas scattered light from dust grains in the surface layer of a circumstellar disk is polarized. Since self-luminous sub-stellar objects and exoplanets are expected to be unpolarized while light from spatially resolved circumstellar disks is 25 − 50% polarized comparison of total and polarized intensity imaging can serve as a powerful tool for disentangling planet signals from disk sub-structure.
Describe the main ideas behind principal components analysis, LOCI, and classic PSF subtraction.
LOCI: Least squares approximation! The model PSF is a linear combination of reference PSFs that is subtracted from your target image divided intro optimisation zones such that the linear coefficients is locally optimised to avoid ovresubtraction in regions without a companion.
PCA: The model PSF uses eigenimages to redice the dimensionality of large datasets.
Classic: Use a suitably chosen other observation as an estimate of the light from the star that you want to get rid of. Need to match the science observation in terms of factors affecting the shape of the PSF, and those affecting temporal variation in the measured PSF.
What is the signal-to-noise ratio of a point source, eg the CCD equation? How does that relate to planet searches?
SNR = [F_obj . (t . QE)^0.5] / [ F_obj + n_pix (F_sky + (F_dark / QE ) + (R^2 / (QE . t)))]^0.5
For planet searches:
SNR_p = F_p . \eta_P . t . PSF(0)/[(F_s . \eta_s . t . PSF(0))^2+ F_p . \eta_P . t . PSF(0) + F_s . \eta_s . t . PSF(0)]
What about in integral-field-spectroscopy?
Assumptions about speckle pattern by each differential imaging technique
RDI: similar stars are observed under similar instrumental set-up assuming the speckle pattern is stable over time
ADI: One assumes that most of the optical aberrations remain static during observations and, come from planes that are optically conjugated with the pupil plane. Keeping the pupil orientation fixed, the speckle pattern is stable in the images whereas the field-of-view rotates around the central star.
SDI: one assumes the speckles are induced by achromatic optical path differences in a pupil plane so that the evolution of the speckle intensity with wavelength is known
PDI: unlike the starlight, the exoplanet light is partially polarized
CDI: One assumes that the speckle pattern is stable over time for temporal modulation of the speckle intensity
