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Robustness of deep learning algorithms in astronomy - galaxy morphology studies
Aleksandra Ciprijanovic · Diana Kafkes · Gabriel Nathan Perdue · Sandeep Madireddy · Stefan Wild · Brian Nord

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in Machine Learning and the Physical Sciences »

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 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.

Author Information

Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory)
Diana Kafkes (Fermi National Accelerator Laboratory)
Gabriel Nathan Perdue (Fermilab)
Sandeep Madireddy (Argonne National Laboratory)
Stefan Wild (Argonne National Laboratory)
Brian Nord (Fermi National Accelerator Laboratory)

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