  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 here 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 novel opportunities
for science observations which would have been challenging to achieve
otherwise.
