Inhomogeneous Dust Biases Photometric Redshifts and Stellar Masses for LSST

Abstract

Spectral energy distribution (SED) modeling is one of the main methods to estimate galaxy properties, such as photometric redshifts, $z$, and stellar masses, $M_{\ast }$, for extragalactic imaging surveys. SEDs are currently modeled as light from a composite stellar population attenuated by a uniform foreground dust screen, despite evidence from simulations and observations that find large spatial variations in dust attenuation due to the detailed geometry of stars and gas within galaxies. In this work, we examine the impact of this simplistic dust assumption on inferred $z$ and $M_{\ast }$ for Rubin LSST. We first construct synthetic LSST-like observations ($ugrizy$ magnitudes) from the NIHAO-SKIRT catalog, which provides SEDs from high-resolution hydrodynamic simulations using 3D Monte Carlo radiative transfer. We then infer $z$ and $M_{\ast }$ from the synthetic observations using the PROVABGS Bayesian SED modeling framework. Overall, the uniform dust screen assumption biases both $z$ and $M_{\ast }$ in galaxies, consistently and significantly for galaxies with dust attenuation $A_V\gtrsim 0.5$, and likely below. The biases depend on the orientation in which the galaxies are observed. At $z=0.4$, $z$ is overestimated by $\sim$0.02 for face-on galaxies and $M_{\ast }$ is underestimated by $\sim$0.4 dex for edge-on galaxies. The bias in photo-$z$ is equivalent to the desired redshift precision level of LSST "gold sample" and will be larger at higher redshifts. Our results underscore the need for SED models with additional flexibility in the dust parameterization to mitigate significant systematic biases in cosmological analyses with LSST.

10 pages, 3 figures; submitted to ApJL; comments welcome