Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models
File(s)
Published version
Author(s)
Leistedt, Boris
Alsing, Justin
Peiris, Hiranya
Mortlock, Daniel
Leja, Joel
Type
Journal Article
Abstract
We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge by the addition of neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission-line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path toward meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric data sets.
Date Issued
2023-01-01
Date Acceptance
2022-10-24
Citation
Astrophysical Journal Supplement Series, 2023, 264 (1), pp.1-12
ISSN
0067-0049
Publisher
American Astronomical Society
Start Page
1
End Page
12
Journal / Book Title
Astrophysical Journal Supplement Series
Volume
264
Issue
1
Copyright Statement
© 2023. 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.
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
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000911840900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Science & Technology
Physical Sciences
Astronomy & Astrophysics
GALAXY COLORS
DISTRIBUTIONS
CALIBRATION
UNCERTAINTIES
PROPAGATION
DUST
COMBINATION
EVOLUTION
SAMPLE
II.
Publication Status
Published
Article Number
ARTN 23
Date Publish Online
2022-10-24