Pop-cosmos: scaleable inference of galaxy properties and redshifts with a data-driven population model
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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 (σMAD = 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.
Date Issued
2024-11-01
Date Acceptance
2024-08-25
Citation
The Astrophysical Journal, 2024, 975 (1)
ISSN
0004-637X
Publisher
IOP Publishing
Journal / Book Title
The Astrophysical Journal
Volume
975
Issue
1
Copyright Statement
© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
10.3847/1538-4357/ad7736
Subjects
ACTIVE GALACTIC NUCLEI
AGN DUSTY TORI
Astronomy & Astrophysics
COMPLETE CALIBRATION
GMASS ULTRADEEP SPECTROSCOPY
HIERARCHICAL BAYESIAN-INFERENCE
LOW-MASS STARS
PHOTOMETRIC REDSHIFT
PHOTO-Z PERFORMANCE
Physical Sciences
Science & Technology
SPECTRAL ENERGY-DISTRIBUTIONS
STAR-FORMING GALAXIES
Publication Status
Published
Article Number
145
Date Publish Online
2024-10-30