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Record W4379518351 · doi:10.3847/2041-8213/acd2d9

CEERS Spectroscopic Confirmation of NIRCam-selected z ≳ 8 Galaxy Candidates with JWST/NIRSpec: Initial Characterization of Their Properties

2023· article· en· W4379518351 on OpenAlexafffund
Seiji Fujimoto, Pablo Arrabal Haro, Mark Dickinson, Steven L. Finkelstein, Jeyhan S. Kartaltepe, Rebecca L. Larson, D. Burgarella, Micaela B. Bagley, Peter Behroozi, Katherine Chworowsky, Michaela Hirschmann, Jonathan R. Trump, Stephen M. Wilkins, L. Y. Aaron Yung, Anton M. Koekemoer, Casey Papovich, Nor Pirzkal, Henry C. Ferguson, A. Fontana, Norman A. Grogin, A. Grazian, Lisa J. Kewley, Dale D. Kocevski, Jennifer M. Lotz, L. Pentericci, Swara Ravindranath, Rachel S. Somerville, R. Amorín, Bren E. Backhaus, Antonello Calabrò, Caitlin M. Casey, Michael C. Cooper, Vital Fernández, Maximilien Franco, Mauro Giavalisco, Nimish P. Hathi, Santosh Harish, Taylor A. Hutchison, Kartheik G. Iyer, Intae Jung, Ray A. Lucas, Jorge A. Zavala

Bibliographic record

VenueThe Astrophysical Journal Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersScience and Technology Facilities CouncilNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyCanadian Space AgencyJet Propulsion LaboratorySpace Telescope Science InstituteNational Science Foundation
KeywordsRedshiftPhysicsGalaxyJames Webb Space TelescopePhotometry (optics)AstrophysicsEmission spectrumAstronomyStarsSpectral line

Abstract

fetched live from OpenAlex

Abstract We present JWST NIRSpec spectroscopy for 11 galaxy candidates with photometric redshifts of z ≃ 9 − 13 and M UV ∈ [ −21, −18] newly identified in NIRCam images in the Cosmic Evolution Early Release Science Survey. We confirm emission line redshifts for 7 galaxies at z = 7.762–8.998 using spectra at ∼1–5 μ m either with the NIRSpec prism or its three medium-resolution ( R ∼ 1000) gratings. For z ≃ 9 photometric candidates, we achieve a high confirmation rate of ≃90%, which validates the classical dropout selection from NIRCam photometry. No robust emission lines are identified in three galaxy candidates at z &gt; 10, where the strong [O iii ] and H β lines would be redshifted beyond the wavelength range observed by NIRSpec, and the Ly α continuum break is not detected with the sensitivity of the current data. Compared with Hubble Space Telescope-selected bright galaxies ( M UV ≃ −22) that are similarly spectroscopically confirmed at z ≃ 8 − 9, these NIRCam-selected galaxies are characterized by lower star formation rates (SFRs; SFR ≃ 4 M ⊙ yr −1 ) and lower stellar masses (≃10 8 M ⊙ ), but with higher specific SFR (≃40 Gyr −1 ), higher [O iii ]+H β equivalent widths (≃1100 Å), and elevated production efficiency of ionizing photons ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>ξ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ion</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi>Hz</mml:mi> <mml:mspace width="0.25em"/> <mml:msup> <mml:mrow> <mml:mi>erg</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> <mml:mo>≃</mml:mo> <mml:mn>25.8</mml:mn> </mml:math> ) induced by young stellar populations (&lt;10 Myr) accounting for ≃20% of the galaxy mass, highlighting the key contribution of faint galaxies to cosmic reionization. Taking advantage of the homogeneous selection and sensitivity, we also investigate metallicity and ISM conditions with empirical calibrations using the [O iii ] 5008 /H β ratio. We find that galaxies at z ≃ 8 − 9 have higher SFRs and lower metallicities than galaxies at similar stellar masses at z ≃ 2 − 6, which is generally consistent with the current galaxy formation and evolution models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.194
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations163
Published2023
Admission routes2
Has abstractyes

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