Probabilistic redshift estimation of undetected galaxies from multi-band background light maps
Year: 2026
Type: article
Abstract: Accurate knowledge of the redshift distributions of undetected galaxy populations is essential for extracting the cosmological information encoded in the cosmic optical background. We present the first framework for estimating these distributions directly from multi-band maps of unresolved background light, using conditional normalising flows trained on realistic image simulations. For imaging quality comparable to that expected from the survey and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), our model achieves sub-per cent accuracy in both the mean and standard deviation of the flux-weighted redshift distributions when the training and target samples are statistically well matched. Furthermore, the network proves highly resilient to incomplete photometric coverage when combined with tailored data imputation strategies. To address the challenge of statistical mismatches between simulations and real observations, we propose incorporating observational quantities into the target variables, providing a built-in diagnostic to assess model reliability and uncertainty from the predictions themselves. We also demonstrate the feasibility of map-based tomographic analyses, showing that the model retains sufficient pixel-level detail to recover redshift distributions for tomographically defined subsamples with near sub-per cent accuracy across all bins. Euclid
Source: Astronomy and Astrophysics
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,865
Funders: U.S. Department of Energy, Deutsche Forschungsgemeinschaft, Generalitat de Catalunya, Institut de Física d'Altes Energies, Office of Science, Natural Sciences and Engineering Research Council of Canada, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Agencia Estatal de Investigación, SLAC National Accelerator Laboratory
OpenAlex ID: W7169585917