Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference

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

doi:10.3847/1538-4365/ac9583

Cited by 14 publications

(18 citation statements)

References 88 publications

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“…Empirical constraints on SFH at high z remain weak, so we think that such simple approach is preferred, allowing data to inform the inference process to the greatest extent. It is feasible to develop a full population model for 0 < z < 1 galaxies (e.g., Alsing et al 2023), and constrain this hyperparameter with data. We leave this improvement for future studies.…”

Section: Discussionmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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: 99%

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

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“…The emulator takes 2-3 ms to model a single SED while the PROVABGS-FSPS requires 300-400 ms. To derive a posterior for a single galaxy, our emulator requires 100× less CPU time, from 10-13 hr to 4-6 minutes. Additional speedup will be achievable using graphics processing units (e.g., Alsing et al 2023) and with more parallel MCMC methods (e.g., affine 7 ). For reproducibility, the codebase for the training, validation, and benchmarking of the emulator is publicly available.…”

Section: Discussionmentioning

confidence: 99%

“…Posteriors of galaxies in a population can also be combined using a Bayesian hierarchical approach for more accurate population inference (e.g., Leja et al 2020Leja et al , 2022Leistedt et al 2023;Alsing et al 2023). However, one major limitation of current Bayesian SED modeling methods is the computational resources that they require.…”

Section: Introductionmentioning

confidence: 99%

Neural Stellar Population Synthesis Emulator for the DESI PROVABGS

Kwon

Hahn

Alsing

2023

ApJS

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The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of >10 million DESI Bright Galaxy Survey galaxies. Each posterior distribution will be inferred from joint Bayesian modeling of observed photometry and spectroscopy using Markov Chain Monte Carlo sampling and the Hahn et al. stellar population synthesis (SPS) model. To make this computationally feasible, PROVABGS will use a neural emulator for the SPS model to accelerate the posterior inference. In this work, we present how we construct the emulator using the Alsing et al. approach and verify that it can be used to accurately infer galaxy properties. We confirm that the emulator is in excellent agreement with the original SPS model with ≪1% error and is 100× faster. In addition, we demonstrate that the posteriors of galaxy properties derived using the emulator are also in excellent agreement with those inferred using the original model. The neural emulator presented in this work is essential in bypassing the computational challenge posed in constructing the PROVABGS catalog. Furthermore, it demonstrates the advantages of emulation for scaling sophisticated analyses to millions of galaxies.

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“…Empirical constraints on SFH at high z remain weak, so we think that such simple approach is preferred, allowing data to inform the inference process to the greatest extent. It is feasible to develop a full population model for 0 < z < 1 galaxies (e.g., Alsing et al 2023), and constrain this hyper-parameter with data. We leave this improvement for future studies.…”

Section: Age-mass-redshift Degeneracymentioning

confidence: 99%

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

Wang¹,

Leja²,

Bezanson³

et al. 2023

Preprint

View full text Add to dashboard Cite

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

Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference

Cited by 14 publications

References 88 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

Neural Stellar Population Synthesis Emulator for the DESI PROVABGS

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

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