Robustness of deep learning algorithms in astronomy -- galaxy morphology
  studies
  Deep learning models are being increasingly adopted in wide array of
scientific domains, especially to handle high-dimensionality and volume of the
scientific data. However, these models tend to be brittle due to their
complexity and overparametrization, especially to the inadvertent adversarial
perturbations that can appear due to common image processing such as
compression or blurring that are often seen with real scientific data. It is
crucial to understand this brittleness and develop models robust to these
adversarial perturbations. To this end, we study the effect of observational
noise from the exposure time, as well as the worst case scenario of a one-pixel
attack as a proxy for compression or telescope errors on performance of
ResNet18 trained to distinguish between galaxies of different morphologies in
LSST mock data. We also explore how domain adaptation techniques can help
improve model robustness in case of this type of naturally occurring attacks
and help scientists build more trustworthy and stable models.
