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Record W2774280499 · doi:10.1051/0004-6361/201732185

Validity of abundances derived from spaxel spectra of the MaNGA survey

2017· article· en· W2774280499 on OpenAlexfundno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratorySmithsonian Astrophysical ObservatoryUniversity of Colorado BoulderInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikUniversidad Nacional Autónoma de MéxicoBrookhaven National LaboratoryMinistério da Ciência, Tecnologia e InovaçãoVolkswagen FoundationDeutsche ForschungsgemeinschaftUniversity of OxfordYork UniversityCarnegie Institution for ScienceCarnegie Mellon UniversityLeibniz-GemeinschaftUniversity of Notre DameKazan Federal UniversityUniversity of ArizonaCollege of Engineering, Michigan State UniversityUniversity of WashingtonPrinceton UniversityAlfred P. Sloan FoundationJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahHarvard UniversityOhio State UniversitySmithsonian InstitutionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityNational Science Foundation
KeywordsSpectral lineGalaxyBalmer seriesSkyLine (geometry)MetallicityObservatoryEmission spectrum

Abstract

fetched live from OpenAlex

We measured the emission lines in the spaxel spectra of Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) galaxies in order to determine the abundance distributions therein. It has been suggested that the strength of the low-ionization lines, R2, N2, and S2, may be increased (relative to Balmer lines) in (some) spaxel spectra of the MaNGA survey due to a contribution of the radiation of the diffuse ionized gas. Consequently, the abundances derived from the spaxel spectra through strong-line methods may suffer from large errors. We examined this expectation by comparing the behaviour of the line intensities and the abundances estimated through different calibrations for slit spectra of H II regions in nearby galaxies, for fibre spectra from the Sloan Digital Sky Survey, and for spaxel spectra of the MaNGA survey. We found that the S2 strength is increased significantly in the fibre and spaxel spectra. The mean enhancement changes with metallicity and can be as large as a factor of ~2. The mean distortion of R2 and N2 is less than a factor of ~1.3. This suggests that Kaufmann et al.’s (2003, MNRAS, 346, 1055) demarcation line between active galactic nuclei and H II regions in the Baldwin, Phillips, & Terlevich (BPT, 1981, PASP, 93, 5) diagram is a useful criterion to reject spectra with significantly distorted strengths of the N2 and R2 lines. We find that the three-dimensional R calibration, which uses the N2 and R2 lines, produces reliable abundances in the MaNGA galaxies. The one-dimensional N2 calibration produces either reliable or wrong abundances depending on whether excitation and N/O abundance ratio in the target region (spaxel) are close to or differ from those parameters in the calibrating points located close to the calibration relation. We then determined abundance distributions within the optical radii in the discs of 47 MaNGA galaxies. The optical radii of the galaxies were estimated from the surface brightness profiles constructed based on the MaNGA observations.

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)0.002
metaresearch head score (Gemma)0.014
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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