  The advent of high-contrast imaging instruments combined with
medium-resolution spectrographs allows spectral and temporal dimensions to be
combined with spatial dimensions to detect and potentially characterize
exoplanets with higher sensitivity. We develop a new method to effectively
leverage the spectral and spatial dimensions in integral-field spectroscopy
(IFS) datasets using a supervised deep-learning algorithm to improve the
detection sensitivity to high-contrast exoplanets. We begin by applying a data
transform whereby the IFS datasets are replaced by cross-correlation
coefficient tensors obtained by cross-correlating our data with young gas giant
spectral template spectra. This transformed data is then used to train machine
learning (ML) algorithms. We train a 2D CNN and 3D LSTM with our data. We
compare the ML models with a non-ML algorithm, based on the STIM map of
arXiv:1810.06895. We test our algorithms on simulated young gas giants in a
dataset that contains no known exoplanet, and explore the sensitivity of
algorithms to detect these exoplanets at contrasts ranging from 1e-3 to 1e-4 at
different radial separations. We quantify the sensitivity using modified
receiver operating characteristic curves (mROC). We discover that the ML
algorithms produce fewer false positives and have a higher true positive rate
than the STIM-based algorithm, and the true positive rate of ML algorithms is
less impacted by changing radial separation. We discover that the velocity
dimension is an important differentiating factor. Through this paper, we
demonstrate that ML techniques have the potential to improve the detection
limits and reduce false positives for directly imaged planets in IFS datasets,
after transforming the spectral dimension into a radial velocity dimension
through a cross-correlation operation.
