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Record W7018283926

THE DEEP2 GALAXY REDSHIFT SURVEY: DESIGN, OBSERVATIONS, DATA REDUCTION, AND REDSHIFTS**Based on observations taken at the W. M. Keck Observatory, which is operated jointly by the University of California and the California Institute of Technology, and on observations made with the NASA/ESO Hubble Space Telescope, obtained from the data archives at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555, and from the Canadian Astronomy Data Centre.

2013· article· en· W7018283926 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsRedshiftGalaxyRedshift surveyPhotometry (optics)Magnitude (astronomy)Apparent magnitude
DOInot available

Abstract

fetched live from OpenAlex

We describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z ∼ 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude MB = -20 at z ∼ 1 via ∼90 nights of observation on the Keck telescope. The survey covers an area of 2.8 deg2 divided into four separate fields observed to a limiting apparent magnitude of RAB = 24.1. Objects with z ≲ 0.7 are readily identifiable using BRI photometry and rejected in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ∼2.5 times more efficiently than in a purely magnitude-limited sample. Approximately 60% of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets that fail to yield secure redshifts are blue objects that lie beyond z ∼ 1.45, where the [O II] 3727 Å doublet lies in the infrared. The DEIMOS 1200 line mm-1 grating used for the survey delivers high spectral resolution (R ∼ 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate observations. Sky subtraction is essentially photon-limited even under bright OH sky lines; we describe the strategies that permitted this, based on high image stability, accurate wavelength solutions, and powerful B-spline modeling methods. We also investigate the impact of targets that appear to be single objects in ground-based targeting imaging but prove to be composite in Hubble Space Telescope data; they constitute several percent of targets at z ∼ 1, approaching ∼5%-10% at z > 1.5. Summary data are given that demonstrate the superiority of DEEP2 over other deep high-precision redshift surveys at z ∼ 1 in terms of redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far. © 2013. The American Astronomical Society. All rights reserved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.042
GPT teacher head0.230
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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

Citations0
Published2013
Admission routes1
Has abstractyes

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