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Record W4226484856 · doi:10.3847/1538-4365/ac6c03

The COS Legacy Archive Spectroscopy Survey (CLASSY) Treasury Atlas*

2022· article· en· W4226484856 on OpenAlexfundno aff
Danielle A. Berg, Bethan L. James, Meaghan McDonald, Zuyi Chen, John Chisholm, Timothy M. Heckman, Crystal L. Martin, Dan P. Stark, Alessandra Aloisi, R. Amorín, Matthew Bayliss, Rongmon Bordoloi, J. Brinchmann, S. Charlot, Jacopo Chevallard, Ilyse Clark, Dawn K. Erb, A. Feltre, Max Grönke, Matthew Hayes, Alaina Henry, Svea Hernández, Anne E. Jaskot, Tucker Jones, Lisa J. Kewley, Nimisha Kumari, Claus Leitherer, Mario Llerena, Michael V. Maseda, Matilde Mingozzi, Themiya Nanayakkara, Masami Ouchi, Adèle Plat, Richard W. Pogge, Swara Ravindranath, Jane R. Rigby, Ryan L. Sanders, Claudia Scarlata, Peter Senchyna, Evan D. Skillman, Charles C. Steidel, Allison L. Strom, Yuma Sugahara, Stephen M. Wilkins, Aida Wofford, Xinfeng Xu

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryBrookhaven National LaboratorySpace Telescope Science InstituteEuropean Southern ObservatoryYork UniversityIstituto Nazionale di AstrofisicaSmithsonian Astrophysical ObservatoryArizona Board of RegentsLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityOffice of ScienceCollege of Engineering, Michigan State UniversityPrinceton UniversityUniversity of WashingtonJohns Hopkins UniversityUniversity of MinnesotaNational Science FoundationHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityLeibniz-Institut für Astrophysik PotsdamNational Aeronautics and Space AdministrationU.S. Department of EnergySmithsonian Institution
KeywordsAtlas (anatomy)TreasurySpectroscopyGeologyGeographyPhysicsArchaeologyAstronomyPaleontology

Abstract

fetched live from OpenAlex

Abstract Far-ultraviolet (FUV; ∼1200–2000 Å) spectra are fundamental to our understanding of star-forming galaxies, providing a unique window on massive stellar populations, chemical evolution, feedback processes, and reionization. The launch of the James Webb Space Telescope will soon usher in a new era, pushing the UV spectroscopic frontier to higher redshifts than ever before; however, its success hinges on a comprehensive understanding of the massive star populations and gas conditions that power the observed UV spectral features. This requires a level of detail that is only possible with a combination of ample wavelength coverage, signal-to-noise, spectral-resolution, and sample diversity that has not yet been achieved by any FUV spectral database. We present the Cosmic Origins Spectrograph Legacy Spectroscopic Survey (CLASSY) treasury and its first high-level science product, the CLASSY atlas. CLASSY builds on the Hubble Space Telescope (HST) archive to construct the first high-quality (S/N1500 Å ≳ 5/resel), high-resolution (R ∼ 15,000) FUV spectral database of 45 nearby (0.002 < z < 0.182) star-forming galaxies. The CLASSY atlas, available to the public via the CLASSY website, is the result of optimally extracting and coadding 170 archival+new spectra from 312 orbits of HST observations. The CLASSY sample covers a broad range of properties including stellar mass (6.2 < log M ⋆(M ⊙) < 10.1), star formation rate (−2.0 < log SFR (M ⊙ yr−1) < +1.6), direct gas-phase metallicity (7.0 < 12+log(O/H) < 8.8), ionization (0.5 < O32 < 38.0), reddening (0.02 < E(B − V) < 0.67), and nebular density (10 < n e (cm−3) < 1120). CLASSY is biased to UV-bright star-forming galaxies, resulting in a sample that is consistent with the z ∼ 0 mass–metallicity relationship, but is offset to higher star formation rates by roughly 2 dex, similar to z ≳ 2 galaxies. This unique set of properties makes the CLASSY atlas the benchmark training set for star-forming galaxies across cosmic time.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.009

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 head0.014
GPT teacher head0.272
Teacher spread0.257 · 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
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

Citations106
Published2022
Admission routes1
Has abstractyes

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