Improved photometric redshift estimations through self-organizing map-based data augmentation

ABSTRACT We introduce a framework for the enhanced estimation of photometric redshifts using self-organizing maps (SOMs). Our method projects galaxy spectral energy distributions (SEDs) onto a 2D map, identifying regions that are sparsely sampled by existing spectroscopic observations. These undersampled areas are then augmented with simulated galaxies, yielding a more representative spectroscopic training data set. To assess the efficacy of this SOM-based data augmentation in the context of the forthcoming Legacy Survey of Space and Time (LSST), we employ mock galaxy catalogues from the OpenUniverse2024 project and generate synthetic data sets that mimic the expected photometric selections of LSST after one (Y1) and ten (Y10) years of observation. We construct 501 degraded realizations of synthetic spectroscopic surveys by sampling galaxy colours, magnitudes, redshifts, and spectroscopic success rates, in order to emulate the diverse compilation of spectroscopic data sets that may exist for LSST analysis. Augmenting the degraded mock data sets with simulated galaxies from the independent CosmoDC2 catalogues significantly improves the performance of our photometric-redshift estimates – particularly at high redshift $(z_\mathrm{true} \gtrsim 1.5)$ – even in the presence of differences in the underlying galaxy SED modelling between the two catalogues. This improvement is manifested in notably reduced systematic biases and a decrease in catastrophic failures by up to approximately a factor of 2, along with a reduction in information loss in the conditional density estimations. These results underscore the effectiveness of SOM-based augmentation in refining photometric redshift estimation, thereby enabling more robust analyses in cosmology and astrophysics for the NSF-DOE Vera C. Rubin Observatory.