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From Observation to Ground Truth: Reconstruction Losses of Galaxy Imaging Under Heteroscedastic Noise
Deep learning is widely used to analyze galaxy images, and the reconstruction loss determines which image features a model prioritizes during training. Mean squared error (MSE) and mean absolute error (MAE) correspond to homoscedastic Gaussian and Laplace likelihoods, respectively, whereas the inver
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