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Record W2531872104 · doi:10.1051/0004-6361/201629031

The physical properties of<i>Spitzer</i>/IRS galaxies derived from their UV to 22<i>μ</i>m spectral energy distribution

2016· article· en· W2531872104 on OpenAlexfundno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryBrookhaven National LaboratoryUniversity of PortsmouthUniversity of California, Los AngelesGeneral Secretariat for Research and TechnologyYork UniversityEuropean Regional Development FundCarnegie Mellon UniversityUniversity of ArizonaOffice of SciencePrinceton UniversityAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityCollege of Engineering, Michigan State UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityYale UniversityVanderbilt UniversityNational Science FoundationEuropean CommissionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationJet Propulsion Laboratory
KeywordsGalaxyLuminous infrared galaxyRedshiftSpectral energy distributionActive galactic nucleusRadio galaxyPhotometry (optics)Peculiar galaxyDisc

Abstract

fetched live from OpenAlex

We provide the basic integrated physical properties of all the galaxies contained in the full Cornell Atlas of Spitzer/IRS Sources (CASSIS) with available broad-band photometry from UV to 22 μm. We have collected broad-band photometric measurements in 14 wavelengths from available public surveys in order to study the spectral energy distribution (SED) of each galaxy in CASSIS, thus constructing a final sample of 1146 galaxies in the redshift range 0 < z < 2.5. The SEDs are modelled with the CIGALE code which relies on the energy balance between the absorbed stellar and the dust emission while taking into account the possible contribution due to the presence of an active galactic nucleus (AGN). We split the galaxies in three groups, a low-redshift (z < 0.1), a mid-redshift (0.1 ≤ z < 0.5) and a high-redshift (z ≥ 0.5) sub-sample and find that the vast majority of the Spitzer/IRS galaxies are star-forming and lie on or above the star-forming main sequence of the corresponding redshift. Moreover, the emission of Spitzer/IRS galaxies with z < 0.1 is mostly dominated by star-formation, galaxies in the mid-redshift bin are a mixture of star forming and AGN galaxies, while half of the galaxies with z ≥ 0.5 show moderate or high AGN activity. Additionally, using rest-frame NUV−r colour, Sérsic indices, optical [OIII] and [NII] emission lines we explore the nature of these galaxies by investigating further their structure as well as their star-formation and AGN activity. Using a colour magnitude diagram we confirm that 97% of the galaxies with redshift smaller than 0.5 have experienced a recent star-formation episode. For a sub-sample of galaxies with available structural information and redshift smaller than 0.3 we find that early-type galaxies are placed below the main sequence, while late-type galaxies are found on the main-sequence as expected. Finally, for all the galaxies with redshift smaller than 0.5 and available optical spectral line measurements we compare the ability of CIGALE to detect the presence of an AGN in contrast to the optical spectra classification. We find that galaxies with high AGN luminosity, as calculated by CIGALE, are most likely to be classified as composite or AGNs by optical spectral lines.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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