Speaker
Description
Deep photometric surveys increasingly rely on low signal-to-noise imaging to detect and characterise faint galaxies, where noise affects source detection, flux measurements, and redshift estimation. Self-supervised denoising methods are attractive because they do not require ground-truth images, but their application to quantitative photometry demands control over systematic flux biases.
We evaluate and improve Noise2Void (N2V) for real astronomical images, using observations from the Physics of the Accelerating Universe Survey (PAUS) and PAUS-like simulations. Standard N2V improves visual quality but systematically underestimates galaxy fluxes, particularly for faint sources. To mitigate this, we modify the N2V architecture with a bounded-output layer and fine-tune the model using low-rank adaptation (LoRA) with two flux-aware loss terms that preserve the flux scale and improve repeated-exposure consistency.
Standard N2V introduces strong flux biases for faint galaxies. Our bounded-output model restores the flux-ratio peak close to the original scale, while the flux-aware LoRA fine-tuning reduces the repeated-exposure flux-difference scatter by 47% relative to the original PAUS uncertainty, keeping the median flux ratio near unity.
Our results show that photometric diagnostics, rather than image-quality metrics alone, are essential for evaluating denoising methods in astronomy. Flux-aware modifications to N2V can effectively reduce biases and improve measurement consistency, demonstrating that self-supervised denoising can be adapted to low-SNR survey images while preserving the flux information needed for quantitative analysis.