Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models

Leistedt, Boris; Alsing, Justin; Peiris, Hiranya V.; Mortlock, D.; Leja, Joel

doi:10.3847/1538-4365/ac9d99

Cited by 9 publications

(5 citation statements)

References 63 publications

(125 reference statements)

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“…For instance, it becomes Third, we compare Prospector to EAzY redshifts. Leistedt et al (2023) also infers photo-z using Prospector-α and compares to EAzY; in this work, they find that a Bayesian hierarchical model is required to achieve similar performance in estimating redshift as EAzY. The fact that the fiducial Prospector model already shows comparable results in Figure 4(a) unambiguously demonstrates the influence of the age-mass-redshift degeneracy.…”

Section: Discussionmentioning

confidence: 56%

“…With regard to photo-z codes, considerable efforts have been devoted to various developments (see Salvato et al 2019;Newman & Gruen 2022 for recent reviews, and also Alsing et al 2023;Leistedt et al 2023 for general discussions on photo-z frameworks). The most common algorithm is inferring redshift by comparing observations to SED templates, as is used in LePhare (Arnouts et al 1999;Ilbert et al 2006), BPZ (Benítez 2000), ZEBRA (Feldmann et al 2006), EAzY (Brammer et al 2008), and others.…”

Section: Introductionmentioning

confidence: 99%

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Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Wang

Leja

Bezanson

et al. 2023

ApJL

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The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies at z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages, and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age–mass–redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey is rendered mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, “Prospector-β,” is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector 9 9 The version used in this work corresponds to the state of the Git repository at commit https://github.com/bd-j/prospector/commit/820ad72363a1f9c22cf03610bfe6e361213385cd. .

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Wang

Leja

Bezanson

et al. 2023

ApJL

Self Cite

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“…Given the extensive discussion on inferring photometric redshifts in the literature (see Newman & Gruen 2022 for a recent review; and Alsing et al 2023 andLeistedt et al 2023 for general discussions on photometric redshift inference), we focus only on the unique issues faced in this work below.…”

Section: Reliability Of the Photometric Redshifts And Future Improvem...mentioning

confidence: 99%

The UNCOVER Survey: A First-look HST+JWST Catalog of Galaxy Redshifts and Stellar Population Properties Spanning 0.2 ≲ z ≲ 15

Wang 王冰,

Leja,

Labbé

et al. 2023

ApJS

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The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster A2744 to create the deepest view of our Universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ∼ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 ≲ z ≲ 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-β priors on the masses and star formation histories to produce joint redshift and stellar populations posteriors. Additionally, lensing magnification is performed on the fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with σ NMAD ∼ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of this Treasury survey, we publicly release the stellar population catalog with this paper, derived from our photometric catalog adapting aperture sizes based on source profiles. This release (the catalog and all related documentation are accessible via the UNCOVER survey web page: https://jwst-uncover.github.io/DR2.html#SPSCatalogs with a copy deposited to Zenodo at doi:10.5281/zenodo.8401181) includes posterior moments, maximum likelihood spectra, star formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.

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“…Thirdly, we compare Prospector to EAzY redshifts. Leistedt et al (2023) also infers photo-z using Prospector-α and compares to EAzY; in this work, they find that a Bayesian hierarchical model is required to achieve similar performance in estimating redshift as EAzY. The fact that the fiducial Prospector model already shows comparable results in Figure 4(a) unambiguously demonstrates the influence of the age-massredshift degeneracy.…”

Section: Age-mass-redshift Degeneracymentioning

confidence: 57%

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Wang¹,

Leja²,

Bezanson³

et al. 2023

Preprint

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The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies out to z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age-mass-redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey renders mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, "Prospector-β", is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector a) .

show abstract

Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models

Cited by 9 publications

References 63 publications

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

The UNCOVER Survey: A First-look HST+JWST Catalog of Galaxy Redshifts and Stellar Population Properties Spanning 0.2 ≲ z ≲ 15

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

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