Orateur
Description
In the absence of systematic errors, weak lensing is among the most constraining probes of dark energy. Blending is a major source of systematic uncertainty for weak-lensing analyses, affecting both galaxy shape measurements and photometric redshifts. At LSST depth, blending is estimated to affect about 62% of detectable galaxies. Deblending therefore requires assigning the observed flux among the individual sources contributing to a scene. Because this source-separation problem is intrinsically underconstrained, deblending methods differ mainly in the assumptions or priors used to regularize the decomposition. Deep-learning methods instead learn galaxy representations directly from data, reducing the need for explicit parametric assumptions about galaxy morphology. Generative approaches can learn a prior from isolated galaxy images and use it to constrain source separation and the reconstruction of individual galaxy images from blended scenes. This raises a central question: whether image reconstruction is itself the most informative endpoint for recovering galaxy properties, or whether these properties can be inferred more accurately directly from the learned latent representation.
We investigate this question using a two-stage variational autoencoder framework based on DebVader. A VAE is first trained on isolated galaxies to learn a generative latent representation, after which the decoder is frozen and a second encoder is trained to map blended observations into this latent space. Our main contribution is a hybrid training strategy in which ellipticity and photometric redshift are predicted directly from the latent representation through supervised MLP heads while image reconstruction is retained as a joint objective.
We find that ellipticity and photometric redshift can be accurately inferred directly from the latent representation of blended scenes. Direct latent-space ellipticity estimation outperforms measurements on reconstructed images, and increasing the relative weight of the ellipticity and redshift prediction losses further improves parameter recovery. At the highest tested weight on the prediction losses, the hybrid model also improves ellipticity recovery from reconstructed images.