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Record W4394620996 · doi:10.3847/2041-8213/ad2f31

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey

2024· article· en· W4394620996 on OpenAlexaff
Micaela B. Bagley, Nor Pirzkal, Steven L. Finkelstein, Casey Papovich, Danielle A. Berg, Jennifer M. Lotz, Gene C. K. Leung, Henry C. Ferguson, Anton M. Koekemoer, Mark Dickinson, Jeyhan S. Kartaltepe, Dale D. Kocevski, Rachel S. Somerville, L. Y. Aaron Yung, Bren E. Backhaus, Caitlin M. Casey, M. Castellano, Óscar A. Chávez Ortiz, Katherine Chworowsky, Isabella G. Cox, Romeel Davé, Kelcey Davis, Vicente Estrada-Carpenter, A. Fontana, Seiji Fujimoto, Jonathan P. Gardner, Mauro Giavalisco, A. Grazian, Norman A. Grogin, Nimish P. Hathi, Taylor A. Hutchison, Anne E. Jaskot, Intae Jung, Lisa J. Kewley, Allison Kirkpatrick, Rebecca L. Larson, Jasleen Matharu, Priyamvada Natarajan, L. Pentericci, Pablo G. Pérez‐González, Swara Ravindranath, Barry Rothberg, Russell E. Ryan, Lu Shen, Raymond C. Simons, Gregory F. Snyder, Jonathan R. Trump, Stephen M. Wilkins

Bibliographic record

VenueThe Astrophysical Journal Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsSaint Mary's University
FundersScience and Technology Facilities CouncilSpace Telescope Science InstituteNational Science Foundation
KeywordsAlgorithmComputer science

Abstract

fetched live from OpenAlex

Abstract We present the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey, a deep slitless spectroscopic and imaging Cycle 1 JWST treasury survey designed to constrain feedback mechanisms in low-mass galaxies across cosmic time. NGDEEP targets the Hubble Ultra Deep Field (HUDF) with NIRISS slitless spectroscopy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>lim</mml:mi> <mml:mo>,</mml:mo> <mml:mi>line</mml:mi> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> <mml:mi>σ</mml:mi> </mml:mrow> </mml:msub> <mml:mo>≈</mml:mo> <mml:mn>1.2</mml:mn> <mml:mspace width="0.25em"/> <mml:mo>×</mml:mo> <mml:mspace width="0.25em"/> </mml:math> 10 −18 erg s −1 cm −2 ) to measure metallicities and star formation rates (SFRs) for low-mass galaxies through the peak of the cosmic SFR density (0.5 &lt; z &lt; 4). In parallel, NGDEEP targets the HUDF-Par2 parallel field with NIRCam ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>lim</mml:mi> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> <mml:mi>σ</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>30.6</mml:mn> <mml:mo>−</mml:mo> <mml:mn>30.9</mml:mn> </mml:math> ) to discover galaxies to z &gt; 12, constraining the slope of the faint end of the rest-ultraviolet luminosity function. NGDEEP overlaps with the deepest HST Advanced Camera for Surveys optical imaging in the sky, F435W in the HUDF ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>lim</mml:mi> <mml:mo>,</mml:mo> <mml:mi mathvariant="normal">F</mml:mi> <mml:mn>435</mml:mn> <mml:mi mathvariant="normal">W</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>29.6</mml:mn> </mml:math> ) and F814W in HUDF-Par2 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>lim</mml:mi> <mml:mo>,</mml:mo> <mml:mi mathvariant="normal">F</mml:mi> <mml:mn>814</mml:mn> <mml:mi mathvariant="normal">W</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>30</mml:mn> </mml:math> ), making this a premier HST+JWST deep field. As a treasury survey, NGDEEP data are public immediately, and we will rapidly release data products and catalogs in the spirit of previous deep-field initiatives. In this paper we present the NGDEEP survey design, summarize the science goals, and detail plans for the public release of NGDEEP reduced data products.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.229
Teacher spread0.195 · 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.

Study designObservational
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

Citations63
Published2024
Admission routes1
Has abstractyes

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