  We present a novel method for detecting outliers in astronomical time series
based on the combination of a deep neural network and a k-nearest neighbor
algorithm with the aim of identifying and removing problematic epochs in the
light curves of astronomical objects. We use an EfficientNet network
pre-trained on ImageNet as a feature extractor and perform a k-nearest neighbor
search in the resulting feature space to measure the distance from the first
neighbor for each image. If the distance is above the one obtained for a
stacked image, we flag the image as a potential outlier. We apply our method to
time series obtained from the VLT Survey Telescope (VST) monitoring campaign of
the Deep Drilling Fields of the Vera C. Rubin Legacy Survey of Space and Time
(LSST). We show that our method can effectively identify and remove artifacts
from the VST time series and improve the quality and reliability of the data.
This approach may prove very useful in sight of the amount of data that will be
provided by the LSST, which will prevent the inspection of individual light
curves. We also discuss the advantages and limitations of our method and
suggest possible directions for future work.
