Kernel phase interferometry is a data analysis technique that allows for the detection of moderate contrast asymmetries below λ/d in high-Strehl images. The technique is of particular interest within the area of planet formation, where the asymmetries around a young star can be from disk features or protoplanet candidates. Here we examine the performance achieved by a kernel phase interferometry program using the SCExAO/CHARIS integral field spectrograph on the Subaru telescope. We investigate the quality of the kernel phases as a function of the Strehl ratio of the observations. We also find that all but the highest quality observations are limited by random, as opposed to systematic errors. Finally we conduct a preliminary analysis of observations of V1247 Orionis, where we tentatively detect the presence of a previously identified companion candidate.
Kernel Phase Interferometry (KPI) is a data processing technique which enhances the angular resolution achievable by a conventional telescope in space or behind a powerful adaptive optics system. KPI is increasingly being applied to observations of young stars, which often host circumstellar disks with complex structure. Since such observations rely on fitting to an interferometric observable (the kernel phase), developing a flexible modelling approach in the form of an image reconstruction code would be greatly beneficial for recovering complex asymmetries. Here we present a proof-of-concept for such an image reconstructor that makes use of automatic differentiation to compute a gradient and Stochastic Gradient Descent (SGD) to find the best fitting image. Using simulated signals, we show that this approach works well for the case of a point-like companion but requires further development to robustly recover extended emission from a disk. This may be made possible by adding one of the many commonly applied regularizers for long-baseline or aperture masking image reconstruction and implementing a more sophisticated variant of gradient descent. Nevertheless, this simple combination of automatic differentiation and SGD shows promise for being a powerful addition to the KPI toolbox.
Kernel phase interferometry (KPI) is a data processing technique that allows for the detection of asymmetries (such as companions or disks) in high-Strehl images, close to and within the classical diffraction limit. We show that KPI can successfully be applied to hyperspectral image cubes generated from integral field spectrographs (IFSs). We demonstrate this technique of spectrally dispersed kernel phase by recovering a known binary with the SCExAO/CHARIS IFS in high-resolution K-band mode. We also explore a spectral differential imaging (SDI) calibration strategy that takes advantage of the information available in images from multiple wavelength bins. Such calibrations have the potential to mitigate high-order, residual systematic kernel phase errors, which currently limit the achievable contrast of KPI. The SDI calibration presented is applicable to searches for line emission or sharp absorption features and is a promising avenue toward achieving photon-noise-limited kernel phase observations. The high angular resolution and spectral coverage provided by dispersed kernel phase offers opportunities for science observations that would have been challenging to achieve otherwise.
Kernel phase interferometry (KPI) is a data processing technique that allows for the detection of assymetries arising from companions of disks in high-Strehl AO-corrected images, close to or beyond the limit of traditional approaches. We show that this technique can be applied to the CHARIS IFS (with SCExAO), by recovering a known binary HD 44927 with CHARIS’s high resolution K-band mode. Currently, KPI is limited by residual systematic errors in the kernel phases. We show that these errors can be mitigated during calibration, by taking advantage of the information available in adjacent wavelength bands. This spectral differential imaging (SDI) calibration method is a promising avenue towards achieving photon noise limited kernel phase observations.
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