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

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 ga...
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Autor*in:	Bingjie Wang [verfasserIn] Joel Leja [verfasserIn] Rachel Bezanson [verfasserIn] Benjamin D. Johnson [verfasserIn] Gourav Khullar [verfasserIn] Ivo Labbé [verfasserIn] Sedona H. Price [verfasserIn] John R. Weaver [verfasserIn] Katherine E. Whitaker [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry

Übergeordnetes Werk:	In: The Astrophysical Journal Letters - IOP Publishing, 2022, 944(2023), 2, p L58
Übergeordnetes Werk:	volume:944 ; year:2023 ; number:2, p L58

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/2041-8213/acba99

Katalog-ID:	DOAJ089156684

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allfields	10.3847/2041-8213/acba99 doi (DE-627)DOAJ089156684 (DE-599)DOAJ806dc5b00dd14dc4aacd01c1690b5944 DE-627 ger DE-627 rakwb eng QB460-466 Bingjie Wang verfasserin aut Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 . Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry Astrophysics Joel Leja verfasserin aut Rachel Bezanson verfasserin aut Benjamin D. Johnson verfasserin aut Gourav Khullar verfasserin aut Ivo Labbé verfasserin aut Sedona H. Price verfasserin aut John R. Weaver verfasserin aut Katherine E. Whitaker verfasserin aut In The Astrophysical Journal Letters IOP Publishing, 2022 944(2023), 2, p L58 (DE-627)312189028 (DE-600)2006858-X 20418213 nnns volume:944 year:2023 number:2, p L58 https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/article/806dc5b00dd14dc4aacd01c1690b5944 kostenfrei https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/toc/2041-8205 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 2, p L58
spelling	10.3847/2041-8213/acba99 doi (DE-627)DOAJ089156684 (DE-599)DOAJ806dc5b00dd14dc4aacd01c1690b5944 DE-627 ger DE-627 rakwb eng QB460-466 Bingjie Wang verfasserin aut Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 . Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry Astrophysics Joel Leja verfasserin aut Rachel Bezanson verfasserin aut Benjamin D. Johnson verfasserin aut Gourav Khullar verfasserin aut Ivo Labbé verfasserin aut Sedona H. Price verfasserin aut John R. Weaver verfasserin aut Katherine E. Whitaker verfasserin aut In The Astrophysical Journal Letters IOP Publishing, 2022 944(2023), 2, p L58 (DE-627)312189028 (DE-600)2006858-X 20418213 nnns volume:944 year:2023 number:2, p L58 https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/article/806dc5b00dd14dc4aacd01c1690b5944 kostenfrei https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/toc/2041-8205 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 2, p L58
allfields_unstemmed	10.3847/2041-8213/acba99 doi (DE-627)DOAJ089156684 (DE-599)DOAJ806dc5b00dd14dc4aacd01c1690b5944 DE-627 ger DE-627 rakwb eng QB460-466 Bingjie Wang verfasserin aut Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 . Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry Astrophysics Joel Leja verfasserin aut Rachel Bezanson verfasserin aut Benjamin D. Johnson verfasserin aut Gourav Khullar verfasserin aut Ivo Labbé verfasserin aut Sedona H. Price verfasserin aut John R. Weaver verfasserin aut Katherine E. Whitaker verfasserin aut In The Astrophysical Journal Letters IOP Publishing, 2022 944(2023), 2, p L58 (DE-627)312189028 (DE-600)2006858-X 20418213 nnns volume:944 year:2023 number:2, p L58 https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/article/806dc5b00dd14dc4aacd01c1690b5944 kostenfrei https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/toc/2041-8205 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 2, p L58
allfieldsGer	10.3847/2041-8213/acba99 doi (DE-627)DOAJ089156684 (DE-599)DOAJ806dc5b00dd14dc4aacd01c1690b5944 DE-627 ger DE-627 rakwb eng QB460-466 Bingjie Wang verfasserin aut Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 . Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry Astrophysics Joel Leja verfasserin aut Rachel Bezanson verfasserin aut Benjamin D. Johnson verfasserin aut Gourav Khullar verfasserin aut Ivo Labbé verfasserin aut Sedona H. Price verfasserin aut John R. Weaver verfasserin aut Katherine E. Whitaker verfasserin aut In The Astrophysical Journal Letters IOP Publishing, 2022 944(2023), 2, p L58 (DE-627)312189028 (DE-600)2006858-X 20418213 nnns volume:944 year:2023 number:2, p L58 https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/article/806dc5b00dd14dc4aacd01c1690b5944 kostenfrei https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/toc/2041-8205 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 2, p L58
allfieldsSound	10.3847/2041-8213/acba99 doi (DE-627)DOAJ089156684 (DE-599)DOAJ806dc5b00dd14dc4aacd01c1690b5944 DE-627 ger DE-627 rakwb eng QB460-466 Bingjie Wang verfasserin aut Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 . Bayesian statistics Computational astronomy Galaxy evolution Galaxy formation Redshift surveys Spectrophotometry Astrophysics Joel Leja verfasserin aut Rachel Bezanson verfasserin aut Benjamin D. Johnson verfasserin aut Gourav Khullar verfasserin aut Ivo Labbé verfasserin aut Sedona H. Price verfasserin aut John R. Weaver verfasserin aut Katherine E. Whitaker verfasserin aut In The Astrophysical Journal Letters IOP Publishing, 2022 944(2023), 2, p L58 (DE-627)312189028 (DE-600)2006858-X 20418213 nnns volume:944 year:2023 number:2, p L58 https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/article/806dc5b00dd14dc4aacd01c1690b5944 kostenfrei https://doi.org/10.3847/2041-8213/acba99 kostenfrei https://doaj.org/toc/2041-8205 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 2, p L58
language	English
source	In The Astrophysical Journal Letters 944(2023), 2, p L58 volume:944 year:2023 number:2, p L58
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title_auth	Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST
abstract	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 .
abstractGer	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 .
abstract_unstemmed	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 .
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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. 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