BayeSED-GALAXIES II. Bayesian full spectrum analysis of galaxies and application in the CSST wide-field slitless spectroscopy survey

Abstract

The China Space Station Telescope (CSST) will conduct wide-field multiband photometric imaging and slitless spectroscopic surveys, advancing cosmology and galaxy evolution studies. Achieving CSST's cosmological goals requires precise redshifts ($\ddot{I}{ƒ}_{\mathrm{NMAD}}\lesssim 0.002-0.005$) from low-resolution ($R\sim 200$) and potentially blended slitless spectra. We present BayeSED3, extended for Bayesian full-spectrum analysis, including nebular emission modeling (via \textsc{Cloudy}) and a Bayesian treatment of the model scaling factor, improving reliability over optimization methods for low SNR spectra. Validated on realistic mock data generated with the CESS emulator (median SNR=1.65, including instrumental and self-blending effects), our method achieves excellent redshift precision with three-band (GU+GV+GI) spectroscopy: $\ddot{I}{ƒ}_{\mathrm{NMAD}}=0.0008$ ($\sim$80% success) for star-forming and $\ddot{I}{ƒ}_{\mathrm{NMAD}}=0.0015$ ($\sim$50% success) for quiescent galaxies. Stellar mass ($\ddot{I}{ƒ}_{\mathrm{NMAD}}\approx 0.015$ dex for SF, $\approx 0.016$ dex for quiescent) and SFR ($\ddot{I}{ƒ}_{\mathrm{NMAD}}\approx 0.05$ dex for SF, especially at SNR>1) are reliably recovered. Self-blending increases scatter by $\gtrsim 30$, but combining spectroscopy with CSST's seven-band photometry significantly improves accuracy, especially for quiescent galaxies and data-limited cases. Single-band spectroscopy plus photometry yields reasonable redshifts: GU+photometry is limited, GI+photometry gives >60% (SF) and >40% (quiescent) success at $\ddot{I}{ƒ}_{\mathrm{NMAD}}\lesssim 0.002$, GV+photometry gives >35% (SF) and $\sim$40% (quiescent) at similar precision. The Bayesian framework offers a powerful method for accurate galaxy characterization, enhancing CSST's scientific outcomes despite the challenges of slitless spectroscopy.

38 pages, including 16 figures and 2 tables; accepted for publication in ApJS. The mock CSST data for ~100,000 galaxies utilized in this work will be publicly available at https://doi.org/10.5281/zenodo.17221343/. A Python script for an in-depth comparison between BayeSED3 and BAGPIPES is available at https://github.com/hanyk/BayeSED3/blob/main/tests/test_bayesed_bagpipes_comparison.py