The Astrophysical Journal (Jan 2024)

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

DOI
https://doi.org/10.3847/1538-4357/ad7736
Journal volume & issue
Vol. 975, no. 1
p. 145

Abstract

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We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pretrained 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 - α , and compare these results to published COSMOS2020 redshift estimates from the widely used EAZY and LePhare codes. For the ∼12,000 galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias (∼10 ^−4 ), high accuracy ( σ _MAD = 7 × 10 ^−3 ), and a low outlier rate (1.6%). We show that the pop-cosmos population model generalizes well to galaxies fainter than its r < 25 mag training set. The sample we have analyzed is ≳3× larger than has previously been possible via posterior sampling with a full SPS model, with average throughput of 15 GPU-sec per galaxy under the pop-cosmos prior, and 0.6 GPU-sec per galaxy under the Prospector prior. This paves the way for principled modeling of the huge catalogs expected from upcoming Stage IV galaxy surveys.

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