With increased adoption of supervised deep learning methods for processing
and analysis of cosmological survey data, the assessment of data perturbation
effects (that can naturally occur in the data processing and analysis
pipelines) and the development of methods that increase model robustness are
increasingly important. In the context of morphological classification of
galaxies, we study the effects of perturbations in imaging data. In particular,
we examine the consequences of using neural networks when training on baseline
data and testing on perturbed data. We consider perturbations associated with
two primary sources: 1) increased observational noise as represented by higher
levels of Poisson noise and 2) data processing noise incurred by steps such as
image compression or telescope errors as represented by one-pixel adversarial
attacks. We also test the efficacy of domain adaptation techniques in
mitigating the perturbation-driven errors. We use classification accuracy,
latent space visualizations, and latent space distance to assess model
robustness. Without domain adaptation, we find that processing pixel-level
errors easily flip the classification into an incorrect class and that higher
observational noise makes the model trained on low-noise data unable to
classify galaxy morphologies. On the other hand, we show that training with
domain adaptation improves model robustness and mitigates the effects of these
perturbations, improving the classification accuracy by 23% on data with higher
observational noise. Domain adaptation also increases by a factor of ~2.3 the
latent space distance between the baseline and the incorrectly classified
one-pixel perturbed image, making the model more robust to inadvertent
perturbations.Comment: 20 pages, 6 figures, 5 tables; accepted in MLS