# Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

arXiv: https://arxiv.org/abs/2111.00961 · HTML: https://mlsanity.com/paper/2111.00961/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification](https://mlsanity.com/paper/2311.01500/) (2023)
- [Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis](https://mlsanity.com/paper/2410.16347/) (2024)
- [Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification](https://mlsanity.com/paper/2411.03334/) (2024)
- [Robustness analysis of Deep Sky Objects detection models on HPC](https://mlsanity.com/paper/2508.09831/) (2025)

## Abstract (arXiv)

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.