Paper: BayeSED-GALAXIES. I. Performance Test for Simultaneous Photometric Redshift and Stellar Population Parameter Estimation of Galaxies in the CSST Wide-field Multiband Imaging Survey
Authors: Yunkun Han, Lulu Fan, Xian Zhong Zheng, Jin-Ming Bai, Zhanwen Han
Figure caption: The Bayesian evidence (BE), maximum likelihood (ML), and metrics of photometric redshift (red cross), stellar mass (blue square), and SFR (green circle) estimation from the Bayesian analysis of the hydrodynamical simulation–based mock galaxy sample for the CSST+Euclid-like survey employing six SED models (0: SFH = τ, –CEH, DAL = Cal+18; 1: SFH = τ, +CEH, DAL = Cal+18; 2: SFH = τ, +CEH, DAL = Sal+18; 3: SFH = β-τ, +CEH, DAL = Sal+18; 4: SFH = β-τ-r, +CEH, DAL = Sal+18; 5: SFH = α-β-τ-r, +CEH, DAL = Sal+18) with increasing complexity in the forms of SFH and DAL, as well as for cases with (bottom panels) and without noise (top panels). In the case without noise, the simplest model “SFH = τ, –CEH, DAL = Cal+18” has the lowest Bayesian evidence of ﹩\mathrm{ln}(\mathrm{BE})=-243,527\pm 6307﹩. Meanwhile, the SED models “SFH = τ, +CEH, DAL = Sal+18” and “SFH = β-τ, +CEH, DAL = Sal+18,” which are neither the simplest nor the most complex models, have the largest Bayesian evidences of ﹩\mathrm{ln}(\mathrm{BE})=72,092\pm 6475﹩ and ﹩\mathrm{ln}(\mathrm{BE})=70,340\pm 6409﹩, which are comparable within error bars. As in the case of the CSST-like survey, the model selection with the maximum likelihood (or equivalently the minimum χ 2) leads to similar results. In contrast to the case without noise, in the case with noise, the two simplest SED models “SFH = τ, –CEH, DAL = Cal+00” and “SFH = τ, +CEH, DAL = Cal+00” have the largest Bayesian evidences of ﹩\mathrm{ln}(\mathrm{BE})=-31\text{,}783\pm 3314﹩ and ﹩\mathrm{ln}(\mathrm{BE})=-31,347\pm 3302﹩, which are comparable within error bars. However, the model selection with the maximum likelihood (or equivalently the minimum χ 2) leads to exactly the opposite results, where the more complex (or flexible) models are always more favored. Generally, the quality of parameter estimates is closely related to the level of Bayesian evidence, which is especially clear in the more realistic case with noise. The model selection with Bayesian evidence is still more consistent with the measurements of the quality of parameter estimation than that with the maximum likelihood (or equivalently the minimum χ 2). For photometric redshift, stellar mass, and SFR, accurate estimation becomes increasingly difficult, with the latter two being more sensitive to the selection of SED models. In the case without noise, the SED models “SFH = τ, +CEH, DAL = Sal+18” and “SFH = β-τ, +CEH, DAL = Sal+18,” which are just the two with the largest Bayesian evidence, give the highest-quality parameter estimates. In the case with noise, the two simplest SED models “SFH = τ, –CEH, DAL = Cal+00” and “SFH = τ, +CEH, DAL = Cal+00,” which are also the two with the largest Bayesian evidence, give the highest-quality parameter estimates. All of these results are very similar to those for the CSST-like survey. However, the relative errors of the Bayesian evidence is reduced, especially in the case without noise. Besides, the quality of parameter estimation, especially that of stellar mass estimation, is significantly improved. Furthermore, the quality of parameter estimation, especially that of stellar mass estimation, increases more slowly with the increase of SED model complexity.