pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model

pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
Stephen Thorp, Justin Alsing, Hiranya V. Peiris, Sinan Deger, Daniel J. Mortlock, Boris Leistedt, Joel Leja, Arthur Loureiro
arXiv:2406.19437v1 Announce Type: new
Abstract: We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-$alpha$, and compare these results to published COSMOS2020 redshift estimates from the widely-used EAZY and LePhare codes. For the $sim 12,000$ galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias ($sim10^{-4}$), high accuracy ($sigma_text{MAD}=7times10^{-3}$), and a low outlier rate ($1.6%$). We show that the pop-cosmos population model generalizes well to galaxies fainter than its $rarXiv:2406.19437v1 Announce Type: new
Abstract: We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-$alpha$, and compare these results to published COSMOS2020 redshift estimates from the widely-used EAZY and LePhare codes. For the $sim 12,000$ galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias ($sim10^{-4}$), high accuracy ($sigma_text{MAD}=7times10^{-3}$), and a low outlier rate ($1.6%$). We show that the pop-cosmos population model generalizes well to galaxies fainter than its $r